AI Gives Us Answers Before We Know What We Are Reading

A Sentence I Couldn't Leave in the Room

I was sitting in a presentation when the speaker said, "Most people who are using AI, I mean a chatbot, have no idea what AI is and how it works."

My immediate reaction was agreement. I had heard versions of the same conversation too many times, especially from executives who announced that their companies needed AI but couldn't explain what they wanted it to improve. They wanted the technology before they had identified the problem. Sometimes they couldn't tell a language model from a search engine, an automation system, or a piece of ordinary software with an AI label attached to it.

As I listened, several questions began to crowd out the presentation.

How many people who use chatbots understand, even approximately, what happens between a prompt and an answer? How many treat ChatGPT as an expanded search box while imagining that something more intelligent and authoritative is taking place? Do people understand that a fluent answer can be wrong, or does fluency create its own kind of evidence? Has anyone measured this, or are those of us who work with AI simply enjoying the feeling that we understand it better than everyone else?

Then the speaker moved to Google Search. Millions of people, he argued, read the AI-generated summary at the top of the page as though it were the search result itself. They don't necessarily recognize it as a model-generated interpretation of information selected by another automated system.

That claim stayed with me. I wanted to know whether the research supported it.

Awareness Arrived Before Understanding

My search first took me to Pew's early ChatGPT numbers. They were useful in 2023. Now they feel like evidence from another technological era, and Pew's own trend line shows why: the share of American adults who use ChatGPT has more than doubled since 2023. Three years is a long time in generative AI. Products have changed, adoption has spread, and behaviors that looked experimental have become ordinary.

Pew has since returned to the subject. Its 2026 survey found that 49 percent of American adults use AI chatbots, up from 33 percent in 2024, though Pew changed the question wording in between, so I read that jump as approximate. Forty-two percent use chatbots to search for information. Sixty percent say they have ever read the AI summaries at the top of search results.

Those numbers told me how common the behavior had become. They didn't tell me whether people understood the systems producing the answers.

Pew found that 96 percent of Americans have heard at least a little about AI, but only 48 percent say they've heard a lot. Asked what technology comes to mind first when they think of AI, 29 percent said chatbots, the most common answer. Only 6 percent named generative AI or large language models, and 3 percent named search engines.

That gap caught my attention. AI has become familiar as a product category without necessarily becoming familiar as a technical concept. People know the names of the tools. They know what to type into them. That's different from understanding what the tools are doing, and Pew's survey doesn't measure that.

I Dug Past the Surveys

I kept looking because adoption surveys couldn't answer the question that had formed during the presentation. I wanted to know whether researchers had examined the mental models people carry into their conversations with chatbots.

That led me to a systematic review of how nonexperts perceive AI chatbots, published in 2026. The authors screened the literature from 2023 and 2024 and ended up with 28 empirical studies. Only seven examined what people know and are aware of: awareness of the tools and an understanding of what they can and can't do. Seventeen of the 28 studied people in academic settings, mostly students and faculty. The authors themselves call knowledge and awareness underexplored, especially outside universities.

Twenty-eight studies, and only seven asked people what they understood about the thing they were using. That's the finding I can't shake.

The review described a consistent pattern of overestimation tied to limited understanding. Some people credited chatbots with humanlike comprehension. Others assumed the quality of an answer depends only on the training data, or that the system was pulling information in real time, which the authors note was incorrect at the time. Why people overestimate, the authors say, is still unexplained.

What struck me most wasn't that users held misconceptions. Almost everyone has an incomplete mental model of the technology they use. The real question is what a misconception costs, and here the review can't say. It didn't test whether misconceptions change how people judge an individual answer. Public understanding of AI has already become the subject of sweeping claims, while the knowledge of ordinary users remains poorly measured.

That changed my own question. I no longer wanted to know whether people were too stupid to use AI. I wanted to know what happens when a technology is designed to feel easier to understand than it really is.

A caution before I go on. These studies were published in 2026, but the data behind them is older. The review covers 2023 and 2024 literature, and the two studies below were run in 2024. I called Pew's 2023 numbers another era, and the same standard applies here. Read what follows as a picture of how people understood chatbots then, not proof of what they understand now.

The first surveyed 568 undergraduates in spring 2024, some at one large public university in California and the rest recruited online across the United States. Most were using the free version of ChatGPT, then GPT-3.5. About 60 percent used chatbots weekly or daily. Many overestimated what the chatbots could do, especially with reasoning and counting, and often treated them as humans or as search engines. Students with more conceptual knowledge overestimated less. One cluster, 28 percent of the students, mixed humanlike thinking with the belief that a chatbot searches a database for its answers.

That's a partial answer to my expanded-search-box question. It isn't a population number, and 44 percent of the sample were computer science majors, so it isn't a typical group either.

Heavier users didn't look worse, by the way. In both samples, more frequent use went along with higher knowledge scores. In one sample, it also went along with more overestimation, and the authors say they can't tell which way the cause runs.

The second study, published in Frontiers in Computer Science in September, interviewed 21 adults with no technical background in late 2024. Every one of them held at least one misconception about how ChatGPT works. Nine described something close to lookup: a question goes in, the system searches a database, an answer comes out. Ten used technical vocabulary, words like "probabilities" or "large language model," and seven of those ten explained the same mechanism in a way that contradicted the term. One talked about word-pair probabilities and then explained that the system looks those pairs up in a list, a database, or on the internet. The authors call this chauffeur knowledge: using the term without understanding what it names.

That's the result that fits my title. Frequent use can make someone fluent in prompting, and even in the vocabulary, while leaving a false picture of what happens after the prompt is submitted. It's 21 people, not a population. But it's the closest thing I found to the mental model behind the expanded search box.

Google's AI Overviews Broke My Trust

My own experience with Google's AI-generated summaries has become bad enough that I sometimes read them to see what they've missed.

Recently, most of the summaries I run into omit a decisive fact, flatten an important exception, or build a bridge between pieces of information that don't support the conclusion. The wording stays polished. The confidence stays intact. The checking hasn't kept up with either.

When I put the same question to the paid versions of ChatGPT or Claude and ask for web research, I often get a far better answer. It's more complete, the sources are easier to inspect, and contradictions are more likely to be acknowledged. Those systems still make mistakes, but the difference in effort is often visible.

My experience is personal evidence, not a statistical finding. A 2026 audit of Google AI Overviews, a preprint still under review, issued 55,393 trending queries over 40 days in spring 2026 and broke the answers into about 98,000 claims. It found that 11 percent of those claims weren't supported by the pages cited alongside them, and omission was the most common failure. That doesn't show that most summaries are wrong, so I can't turn my frustration into a universal conclusion. It does show unsupported statements at a scale that deserves more than a casual warning label.

The same audit found that the domains Google cites were more credible than the ordinary first-page results, and that source quality said little about whether a claim was faithful to its source. So weak sources don't explain the unsupported claims. Something else happens between the source and the sentence.

Pew's browsing data adds another piece. In March 2025, when a Google AI summary appeared, users clicked a conventional search result in only 8 percent of visits, compared with 15 percent when no summary appeared. They clicked a source inside the summary just 1 percent of the time. That data is a year and a half old, so I treat it as a baseline, not a current reading.

The summary doesn't need to convince everyone that it's infallible. It only needs to reduce the impulse to continue searching.

Now back to the speaker's claim. He said millions of people read the summary as though it were the search result itself. I never found a study that measures that, and I looked. The audit's authors write that AI Overviews reach more than 2 billion users who may not realize the answers are AI-generated, but that's an assertion, not a measurement. What I found is adjacent: six in ten adults say they read these summaries, and in the browsing data almost nobody clicks a source inside one. Whether they take the summary for the result or for an interpretation, I can't tell you.

What Google Is Actually Showing Us

There's a technical correction worth making. An AI Overview isn't a model sitting alone and guessing without access to information. Google Search retrieves and ranks material, and a generative model uses selected information to build a summary. The result combines search infrastructure with language generation. But the two parts don't line up as neatly as I first assumed. The audit found that nearly 30 percent of the domains cited in AI Overviews didn't appear on the first page of ordinary results at all. The summary draws on evidence chosen by a different process than the links below it.

That architecture can fail at several points. The search system may retrieve incomplete material. The model may omit a condition that changes the conclusion. It may connect two statements too aggressively or attach a citation that supports only part of the sentence.

Calling the summary "search results" hides those transformations. Calling it a guess hides the retrieval system underneath it. I think of it as a generated interpretation of machine-selected evidence. That description is less convenient, but it's closer to what the user is actually reading.

The distinction between a link and a synthesis has practical consequences. A traditional result points toward a source and leaves the reading to me. An AI Overview performs part of the interpretation before I see the source, and often before I decide whether I need one. By the time the links appear, the answer has already framed the issue.

People Can Distrust AI and Still Rely on It

Calling users stupid would be emotionally satisfying for about five minutes and intellectually lazy for much longer. Researchers have a better description for the behavior: miscalibrated reliance.

The problem isn't merely whether someone trusts or distrusts AI. It's whether the level of trust matches the system's actual reliability in that situation.

Public attitudes are more skeptical than the stereotype of the enchanted chatbot user suggests. In AP-NORC's 2026 survey (1,036 adults, fielded July 29 to August 10, margin of error 4.1 points), only 9 percent were extremely or very confident that information from AI, chatbots, or search results is reliable and factual. Forty-nine percent were not very or not at all confident. And just 20 percent said it's easy to verify whether AI-generated information is accurate. The question lumps AI in with search results, so it isn't an AI-only number, but the direction is clear.

Yet people keep using these tools. That's not necessarily contradictory.

A person may know in the abstract that AI can be wrong while accepting a particular answer because it looks plausible, sits at the top of the page, and saves several minutes of work. General skepticism can disappear at the exact moment verification becomes inconvenient.

A large preregistered experiment on trust in AI search, also a preprint, found the same split. On average, participants trusted generative search less than traditional search. But reference links and citations raised their trust in the AI answers, even when the citations were incorrect or hallucinated. The visual presence of a citation can stand in for checking it. A source-shaped object creates reassurance before anyone discovers whether the source exists or supports the claim.

This isn't unique to AI. People rely on interface signals everywhere. We assume the top search result is more relevant, that a professional layout reflects professional work, and that confident language reflects confident knowledge. Generative AI intensifies the problem by producing all of those signals at once.

The user bears responsibility for important decisions. Product designers also bear responsibility for systems that present uncertainty as smooth prose and place generated synthesis in the most authoritative position on the page.

Accuracy Has a Price

My first explanation for the gap between Google's summaries and the paid research tools was blunt: of course the paid products are better. Better models, longer reasoning, more searches, and more source processing cost money.

The basic economics are real, but the conclusion needs care. In a June 2025 interview, a Google distinguished engineer described an AI Overview as a very complex system that farms a query out to a bunch of models, and said the company is trying to make inference as affordable as possible. OpenAI's documentation is just as direct: higher reasoning effort gives more thorough answers on hard tasks and costs more in tokens, time, and money. Anthropic bills developers separately for model use and for web searches, and Research is limited to paid consumer plans.

Money isn't the whole story, though. Google said in January that Gemini 3 became the default model behind AI Overviews, and Anthropic's default Sonnet 5 is available on free plans as well as paid ones. The model name alone can't explain the difference. The product also decides how much time the model gets, how many sources it inspects, and how hard the system is tuned for speed. And more spending doesn't buy accuracy at a fixed rate: a Google Research paper on search agents is about reaching higher accuracy with fewer tool calls and lower cost.

A subscription doesn't purchase truth. It can purchase access to a more resource-intensive attempt at finding it. So I treat free, instant AI summaries as a higher-risk information product. That's my conclusion from the incentives, not a law established by one study. Nobody promised users that a free search box would do a paid research run. Nobody told them it wouldn't, either.

Executives Are Not Outside the Problem

The speaker's remark also brought back the executives I've met who wanted AI because other organizations were talking about it.

Gartner's numbers don't measure that behavior, but they rhyme with it. In its survey of 456 CEOs and other senior executives, run between June and November 2024, 77 percent of CEOs said AI is ushering in a new business era. At the same time, CEOs considered only 44 percent of their chief information officers to be AI-savvy. They named the inability to hire enough skilled people and the inability to calculate value as the top limits on deploying AI. And 66 percent of CEOs said their business models weren't fit for AI purposes.

Those figures reflect executives' perceptions rather than an objective examination of their technical knowledge, and they're almost two years old. Even so, the pattern is recognizable. Ambition has moved faster than comprehension.

I've sat in conversations where the request was effectively, "We need AI. Tell us what to do with it." That reverses the order in which good decisions are usually made. The organization has already chosen the answer and is now searching for a question worthy of it.

Executive misunderstanding is more consequential than a consumer accepting a bad summary about a restaurant or a household repair.

It can shape procurement, staffing, customer service, risk management, and the way employees are evaluated. The person who can't explain what a model does may still be authorized to purchase it for thousands of others.

Knowing When an Answer Is Not Knowledge

I left the research with less contempt for users and more concern about the environment in which they're being asked to make judgments. Millions of people now interact with AI through interfaces that require almost no technical understanding. That accessibility is one of the technology's great achievements. It's also the source of a dangerous illusion. When a system is easy to operate, we begin to assume that its output is easy to evaluate.

It isn't. A chatbot can be useful without understanding the question as a person would. A search summary can be based on real sources while distorting what those sources say. A paid research product can spend more on an answer and still be wrong. None of that makes the tools worthless. It defines the conditions under which they deserve our confidence.

I still agree with part of what the speaker said. Many people use chatbots without knowing how they work. The research supports that concern, though it can't give a clean percentage for the whole population. His second claim, that people take the Google summary for the result itself, I couldn't confirm. I couldn't find anyone who has measured it.

Where I disagree is with the temptation to turn that finding into a judgment about intelligence.

People are responding to products built to conceal their complexity. They see an answer before they see a process, a summary before they see evidence, and a citation before they decide whether to inspect it.

My question is no longer whether users are smart enough for AI. It's whether AI products are honest enough about the work they haven't done.