About seven years ago, we asked Hungarians whether they associated the term artificial intelligence more with everyday or abstract concepts, and whether they saw it as something of the near or distant future. The results were unsurprising: roughly twice as many respondents perceived artificial intelligence (AI) as distant and abstract rather than close and part of everyday life. Remarkably, around one-third of respondents even considered it impossible that an AI could beat a human in a complex strategy game like chess within their lifetime—despite the fact that this had already occurred well before the survey.

In fact, it was back in 1997 that IBM’s Deep Blue defeated international chess grandmaster Garry Kasparov, who had already won the world championship in his early 20s. Two decades later, in 2017, Google’s AI, AlphaZero, learned to play chess in just a few hours and went on to defeat the reigning (machine) champion.

Although artificial intelligence had long been part of our lives, its presence was not as visible or widely recognized. In recent years, however, the situation has changed dramatically—largely due to the emergence of ChatGPT. Since its launch in 2022, generative language models, especially ChatGPT, have become synonymous with AI for many. If we were to ask the same questions today as we did seven years ago, few people would likely describe AI as abstract or belonging to a distant future. Its presence is now undeniable, and the pace of AI-driven innovation is extraordinary.

ChatGPT itself has experienced rapid user growth: today, it boasts over 500 million weekly active users worldwide. More than 15.5 million users subscribe to premium services, and 1.5 million corporate clients use the platform. Over 1 billion queries are processed daily. And this is just the most well-known AI application—new AI-powered solutions are emerging almost daily. The technology news cycle is now dominated by AI-related developments—and, increasingly, AI-related hype.

‘Despite the prominence of artificial intelligence in public discourse, it does not top the list of topics respondents believe should be discussed more’

In a 2025 survey conducted by the Youth Research Institute, we analysed the views of 15 to 39-year-olds in the United States and Hungary, asking them which topics they believe are most ‘trending’ today. Artificial intelligence stood out: 76 per cent of Americans and 88 per cent of Hungarians said it is talked about much more now than five years ago. While the difference in percentages is notable, AI eclipses all other topics—such as migration, mental health, and climate change.

Among Americans, migration was the second most frequently mentioned topic (67 per cent), while Hungarians pointed to climate change and mental health (52 per cent each). These results reveal not only how seriously different issues are perceived, but also how attention is distributed across them. Perhaps most striking, however, is that despite the prominence of artificial intelligence in public discourse, it does not top the list of topics respondents believe should be discussed more. Of the eight topics surveyed, the highest percentage of agreement is recorded for mental health, which ranks first among Americans and second among Hungarian 15–39-year-olds.

Despite the many differences, a degree of devaluation of AI is evident in both the American and Hungarian samples: only 37 per cent of Americans and 21 per cent of Hungarians aged 15 to 39 expressed a desire to engage more in discussions about artificial intelligence. In contrast, Hungarian respondents indicated strong interest in topics such as the future (70 per cent) and family (52 per cent), alongside mental health. Among Americans, climate change (50 per cent) and the future (46 per cent) were identified as the most important issues for further discussion.

What does all this mean? Have we already grown tired of artificial intelligence? A few years ago, we were putting it off into the distant future, and now we barely want to mention it.

According to Anthony Downs’ observations on public opinion in the US,¹ public attention rarely focuses on a single topic for long. This is true even when it is an ongoing issue of central importance to society. Instead, a systematic ‘issue-attention cycle’ governs the dynamics of public opinion. This means that a particular issue suddenly comes to the fore and receives intense attention for a short period of time, only to gradually fade from view, largely unresolved. The main reason for this decline in attention is that such problems typically require costly, complex, or inconvenient solutions—so it is easier to pursue an ostrich policy.

In recent years, this phenomenon has been most evident in the context of climate change. Climate alarmism led to demonstrations around the world, with young protesters joining scientists and experts in calling attention to the issue. The media agenda—both legacy and digital—was dominated by climate change coverage. Today, however, this heightened interest is far less visible. It might even give the impression that a climate catastrophe has been averted, that the problem has somehow been solved. Fridays for Future, once an organizer of global protests, is now barely visible, and the large-scale demonstrations and major headlines are notably absent. Even Greta Thunberg, once a central figure in the climate movement, has shifted her focus to other issues currently in the media spotlight. If we consider the objective facts, our planet is likely in a worse condition today than when the climate strikes began—but the issue no longer feels as urgent. Have we grown tired of the climate crisis?

The literature on the impact of technological innovation now includes the term ‘AI fatigue’, which describes our growing inability to keep up with developments in artificial intelligence. New applications and constant media coverage create a level of informational noise that is no longer stimulating but overwhelming, leading to disengagement. Researchers at Washington State University reported last year that the inclusion of the term ‘artificial intelligence’ in product or service descriptions is now associated with a decrease in purchase intent (Cicek et al., 2024).² And AI fatigue is not limited to average consumers; numerous articles have described how professionals in the tech sector themselves struggle to keep pace with the rapid evolution of AI.

‘Artificial intelligence cannot advance faster than we humans are able—or willing—to adapt to it’

In his theory of the diffusion of innovation, Rogers referred to those who are the last to adopt new technologies as ‘laggards’.³ But what happens if the majority of society feels unable to keep up with the accelerating pace of technological development? Will everyone become a laggard? Hardly. The assumption that the speed of technological progress goes hand in hand with the speed of social adaptation is not particularly convincing. Artificial intelligence cannot advance faster than we humans are able—or willing—to adapt to it. There is little value in developing newer and newer solutions if they go unused. There is little point in having a device in one’s pocket that provides access to all of humanity’s knowledge if it is used only to watch funny cat videos.

Rogers rightly recognized that the spread of innovation depends heavily on whether it provides clear benefits to users, is easy to learn, and is compatible with existing technologies. Moreover, no innovation can succeed without the will and engagement of a large user base. The growing phenomenon of AI fatigue and public disinterest suggests that the hype around artificial intelligence may have arrived too fast or too early—relative to what this technology is actually able to deliver.

1. Anthony Downs, ‘Up and Down with Ecology-the Issue-Attention Cycle’, Public Interest, 28, 1972, pp. 38–50. ↩︎
2. Mesut Çiçek; Dogan Gursoy; & Lu Lu, ‘Adverse impacts of revealing the presence of “Artificial Intelligence (AI)” technology in product and service descriptions on purchase intentions: the mediating role of emotional trust and the moderating role of perceived risk’, Journal of Hospitality Marketing & Management, 34(1), 2024, pp. 1–23. ↩︎
3. Everett M Rogers, Diffusion of Innovations (5th ed), New York, Free Press, 2003. ↩︎

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