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The May 29, 2024 headline “Most People Couldn’t Give a Crap About Using AI” was based on real research, but it put a sharper spin on the findings than the survey itself supports. Researchers measured awareness, use and expectations—not whether people cared. Their results showed that generative AI was still unfamiliar or used only occasionally by many online respondents, even as people expected it to affect society. A 2025 follow-up found substantially higher use.
What the survey actually measured
The headline came from a study by Richard Fletcher and Rasmus Kleis Nielsen of the Reuters Institute for the Study of Journalism at the University of Oxford. It surveyed roughly 12,000 online respondents—about 2,000 each in Argentina, Denmark, France, Japan, the United Kingdom and the United States. The researchers asked about awareness of named generative-AI tools, personal use and frequency, uses such as making media or finding information, expected effects on society, and views about AI in journalism. The Reuters Institute report is the primary source for the findings.
That scope matters. This was a survey of online populations in six countries, not a worldwide census, and it focused on generative-AI products people could identify—not every service or feature that might use machine learning behind the scenes.
Low use was real; indifference was not what they measured
ChatGPT was the best-known and most-used of the named tools, but only around half of respondents had heard of it. Between 20% and 30% had not heard of any of the leading named AI products. Daily ChatGPT use was uncommon: it ranged from about 1% in Japan to 7% in the United States, with figures around 2% in France and the UK.
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Many people who had tried a tool had used it only once or twice. Across the six countries, about 28% said they had used generative AI to create some kind of media—such as text, code, images, video or audio—and about 24% had used it to obtain information. Roughly 5% had used it to get the latest news. These measures describe different activities; they should not be added together as if they were mutually exclusive groups.
None of those figures answers whether respondents “gave a crap.” Not having heard of a chatbot is not hostility toward it. Trying it once is not regular adoption, while uncertainty about its output is not the same as distrust. A person might have no reason to seek out a standalone chatbot, lack confidence in its answers, or simply not know enough to have a view. The survey’s results cannot distinguish all those explanations as a single measure of interest.
They also do not show that respondents had never encountered AI. People can use AI-enabled functions in familiar services without recognizing or describing them as AI. The survey’s named-product questions are useful for understanding adoption of consumer-facing generative tools, but they are not a count of every contact with automated systems.
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People expected AI to matter, even if they did not use it much
The most important complication for the headline is the gap between personal use and expected social impact. Across the six countries, 51% to 66% expected generative AI to have a large impact on major sectors over the next five years. Expectations were especially high for fields including science and news media. Respondents were generally more optimistic about uses in science and healthcare, and more cautious about AI in news and journalism. Concerns about jobs also featured prominently.
In other words, someone could rarely use a chatbot and still expect AI to affect their work, the news they read or the services they rely on. Low personal adoption is not evidence of low perceived importance. The stronger reading of the 2024 findings is that AI had attracted much more attention from technology companies than it had become a routine part of many people’s lives—but people were not necessarily unconcerned about its consequences.
Trying ChatGPT did not necessarily mean becoming a regular user
Age was the clearest divide in reported ChatGPT trial: 56% of respondents aged 18–24 said they had used it at least once, compared with 16% of those aged 55 and older. Younger respondents were also more likely to expect AI to change their own lives. Use was somewhat more common among men and people with higher formal education.
Those are differences in reported use and expectations, not a verdict that young people love AI or older people reject it. “Ever tried” includes a one-off experiment; it says little by itself about frequency, trust or whether someone wants AI involved in a particular decision.
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AI in journalism is not one thing
The survey’s journalism focus makes it especially important to separate different uses. There is a meaningful difference between AI helping with transcription, translation, search or data processing; a journalist using it to assist with research or drafting; an AI system producing a story that a person reviews; and mostly automated news with little human editorial oversight.
The Reuters Institute’s 2024 reporting on public attitudes toward AI in journalism found a general preference for human journalists to remain in control. Audiences were more comfortable with AI assisting journalists than with news being produced mostly by AI with limited human oversight. That distinction is not a blanket rejection of AI: acceptance depends on what the tool does, how visible it is and how much human responsibility remains.
Why industry excitement and everyday use could diverge
The survey documents the gap; it does not prove why it existed. Several explanations are plausible. In early 2024, standalone generative-AI tools were still relatively new, often required people to seek them out, and did not solve an obvious problem for everyone. Corporate investment, publicity and rapid product launches could also make AI seem more embedded in ordinary routines than it was for many respondents.
There is a further measurement wrinkle: people may encounter algorithmic systems without knowing they are AI, and may care about outcomes—such as a faster service, reliable health information or job security—without caring much about the label. Those are reasonable interpretations, not findings the survey directly established.
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The 2024 results should not be presented as a current adoption snapshot. In a 2025 follow-up using the same six-country framework, the Reuters Institute reported that the share saying they had ever used a standalone generative-AI system rose from 40% to 61%, while weekly use increased from 18% to 34%. See the 2025 Generative AI and News report.
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That comparison shows a substantial increase in use, not that every country or group changed in the same way, or that growing use automatically brought confidence. Skepticism about AI’s role in journalism and uncertainty about its outputs remained relevant. It is also a comparison between survey waves, not a panel proving that the same individuals changed their minds.
Verdict: a catchy headline, but an overstatement
The 2024 headline had a genuine finding behind it: many respondents had not tried, or had barely used, prominent generative-AI tools. But “couldn’t give a crap” turns limited familiarity and use into a claim about indifference that the researchers did not directly measure. The same respondents often expected AI to have a major social impact, and their comfort depended on the application—especially in journalism, where human oversight mattered. By 2025, standalone AI use had risen markedly. The headline works as a provocative description of early adoption, not as a precise account of public attitudes or a statement about people today.
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