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Generative AI was trained on centuries of art and writing produced by humans. But scientists and critics have wondered what would happen once AI became widely adopted and started training on its outputs. A new study points to some answers. In January 2026, artificial intelligence researchers Arend Hintze, Frida Proschinger ström, and Jory Schossau published a study showing what happens when generative AI systems are allowed to run autonomouslygenerating and interpreting their own outputs without human intervention. The researchers linked a text-to-image system with an image-to-text system and let them iterateimage, caption, image, captionover and over and over. Regardless of how diverse the starting prompts wereand regardless of how much randomness the systems were allowedthe outputs quickly converged onto a narrow set of generic, familiar visual themes: atmospheric cityscapes, grandiose buildings, and pastoral landscapes. Even more striking, the system quickly forgot its starting prompt. The researchers called the outcomes visual elevator musicpleasant and polished, yet devoid of any real meaning. For example, they started with the image prompt, The Prime Minister pored over strategy documents, trying to sell the public on a fragile peace deal while juggling the weight of his job amidst impending military action. The resulting image was then captioned by AI. This caption was used as a prompt to generate the next image. After repeating this loop, the researchers ended up with a bland image of a formal interior spaceno people, no drama, no real sense of time and place. As a computer scientist who studies generative models and creativity, I see the findings from this study as an important piece of the debate over whether AI will lead to cultural stagnation. The results show that generative AI systems themselves tend toward homogenization when used autonomously and repeatedly. They even suggest that AI systems are currently operating in this way by default. The familiar is the default This experiment may appear beside the point: Most people dont ask AI systems to endlessly describe and regenerate their own images. The convergence to a set of bland, stock images happened without retraining. No new data was added. Nothing was learned. The collapse emerged purely from repeated use. But I think the setup of the experiment can be thought of as a diagnostic tool. It reveals what generative systems preserve when no one intervenes. This has broader implications, because modern culture is increasingly influenced by exactly these kinds of pipelines. Images are summarized into text. Text is turned into images. Content is ranked, filtered, and regenerated as it moves between words, images, and videos. New articles on the web are now more likely to be written by AI than humans. Even when humans remain in the loop, they are often choosing from AI-generated options rather than starting from scratch. The findings of this recent study show that the default behavior of these systems is to compress meaning toward what is most familiar, recognizable, and easy to regenerate. Cultural stagnation or acceleration? For the past few years, skeptics have warned that generative AI could lead to cultural stagnation by flooding the web with synthetic content that future AI systems then train on. Over time, the argument goes, this recursive loop would narrow diversity and innovation. Champions of the technology have pushed back, pointing out that fears of cultural decline accompany every new technology. Humans, they argue, will always be the final arbiter of creative decisions. What has been missing from this debate is empirical evidence showing where homogenization actually begins. The new study does not test retraining on AI-generated data. Instead, it shows something more fundamental: Homogenization happens before retraining even enters the picture. The content that generative AI systems naturally producewhen used autonomously and repeatedlyis already compressed and generic. This reframes the stagnation argument. The risk is not only that future models might train on AI-generated content, but that AI-mediated culture is already being filtered in ways that favor the familiar, the describable, and the conventional. Retraining would amplify this effect. But it is not its source. This is no moral panic Skeptics are right about one thing: Culture has always adapted to new technologies. Photography did not kill painting. Film did not kill theater. Digital tools have enabled new forms of expression. But those earlier technologies never forced culture to be endlessly reshaped across various mediums at a global scale. They did not summarize, regenerate and rank cultural productsnews stories, songs, memes, academic papers, photographs, or social media postsmillions of times per day, guided by the same built-in assumptions about what is typical. The study shows that when meaning is forced through such pipelines repeatedly, diversity collapses not because of bad intentions, malicious design or corporate negligence, but because only certain kinds of meaning survive the text-to-image-to-text repeated conversions. This does not mean cultural stagnation is inevitable. Human creativity is resilient. Institutions, subcultures, and artists have always found ways to resist homogenization. But in my view, the findings of the study show that stagnation is a real risknot a speculative fearif generative systems are left to operate in their current iteration. They also help clarify a common misconception about AI creativity: Producing endless variations is not the same as producing innovation. A system can generate millions of images while exploring only a tiny corner of cultural space. In my own research on creative AI, I found that novelty requires designing AI systems with incentives to deviate from the norms. Without it, systems optimize for familiarity because familiarity is what they have learned best. The study reinforces this point empirically. Autonomy alone does not guarantee exploration. In some cases, it accelerates convergence. This pattern already emerged in the real world: One study found that AI-generated lesson plans featured the same drift toward conventional, uninspiring content, underscoring that AI systems converge toward whats typical rather than whats unique or creative. Lost in translation Whenever you write a caption for an image, details will be lost. Likewise, for generating an image from text. And this happens whether its being performed by a human or a machine. In that sense, the convergence that took place is not a failure thats unique to AI. It reflects a deeper property of bouncing from one medium to another. When meaning passes repeatedly through two different formats, only the most stable elements persist. But by highlighting what survives during repeated translations between text and images, the authors are able to show that meaning is processed inside generative systems with a quiet pull toward the generic. The implication is sobering: Even with human guidancewhether that means writing prompts, selecting outputs, or refining resultsthese systems are still stripping away some details and amplifying others in ways that are oriented toward whats average. If generative AI is to enrich culture rather than flatten it, I think systems need to be designed in ways that resist convergence toward statistically average outputs. There can be rewards for deviation and support for less common and less mainstream forms of expression. The study makes one thing clear: Absent these interventions, generative AI will continue to drift toward mediocre and uninspired content. Cultural stagnation is no longer speculation. Its already happening. Ahmed Elgammal is a professor of computer science and director of the Art & AI Lab at Rutgers University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Many people spend an incredible amount of time worrying about how to be more successful in life. But what if thats the wrong question? What if the real struggle for lots of us isnt how to be successful, but how to actually feel successful? Thats the issue lots of strivers truly face, according to ex-Googler turned neuroscientist and author Anne-Laure Le Cunff. In her book Tiny Experiments, she explores how to get off the treadmill of constantly chasing the next milestone, and instead find joy in the process of growth and uncertainty. Youre probably doing better than you give yourself credit for, she explained on LinkedIn recently, before offering 10 telltale signs that what you need isnt to achieve more but to recognize your achievements more. Are you suffering from success dysmorphia? Before we get to those signs, let me try to convince you that youre probably being way too hard on yourself about how well youre doing in life. Start by considering the concept of dysmorphia. Youve probably heard the term in relation to eating disorders. In that context, dysmorphia is when you have a distorted picture of your body. You see a much larger person in the mirror than the rest of the world sees when they look at you. But dysmorphia doesnt just occur in relation to appearance. One recent poll found that 29% of Americans (and more than 40% of young people) experience money dysmorphia. That is, even though theyre doing objectively okay financially, they constantly feel as if theyre falling behind. Financial experts agree that thanks to a firehose of unrealistic images and often dubious money advice online, its increasingly common for people to have a distorted sense of how well theyre actually doing when it comes to money. Or take the idea of productivity dysmorphia, popularized by author Anna Codrea-Rado. In a widely shared essay, she outed herself as a sufferer, revealing that despite working frantically and fruitfully, she never feels that shes done enough. When I write down everything Ive done since the beginning of the pandemicpitched and published a book, launched a media awards, hosted two podcastsI feel overwhelmed. The only thing more overwhelming is that I feel like Ive done nothing at all, she wrote back in 2021. Which means she did all that in just over a year and still feels inadequate. Thats crazy. But its not uncommon to drive ourselves so relentlessly. In Harvard Business Review, Jennifer Moss, author of The Burnout Epidemic, cites a Slack report showing that half of all desk workers say they rarely or never take breaks during the workday. She calls this kind of toxic productivity, a common sentiment in todays work culture. 10 signs of success All together, this evidence paints a picture of a nation that is pretty terrible at gauging and celebrating success. The roots of the issue obviously run deep in our culture and economy. Reorienting our collective life to help us all recognize that there is such a thing as enough is beyond the scope of this column. But in the meantime, neuroscience can help you take a small step toward greater mental peace by reminding you youre probably doing better than you sometimes feel you are. Especially, Le Cunff stresses, if you notice these signs of maturity, growth, and balance in your life. You celebrate small wins. You try again after failing. You pause before reacting. You take breaks without guilt. You recover from setbacks faster. You ask for help when you need it. Youre kind to yourself when you make mistakes. You notice patterns instead of judging them. You make decisions based on values, not pressure. Youre more curious than anxious about whats next. A neuroscientist and a writer agree: Practice becoming Writer Kurt Vonnegut once advised a young correspondent, Practice any art, music, singing, dancing, acting, drawing, painting, sculpting, poetry, fiction, essays, reportage, no matter how well or badly, not to get money and fame, but to experience becoming, to find out whats inside you, to make your soul grow. In other words, artists agree with neuroscientists. Were all works in progress. Youre always going to be in the middle of becoming who you are. You may as well learn to appreciate yourself and the process along the way. We often feel like we need to reach just one more milestone before we can feel successful. But the tme to celebrate isnt when youre arrived at successnone of us fully ever gets thereits at every moment of growth and wisdom along the journey. By Jessica Stillman This article originally appeared in Fast Company‘s sister publication, Inc. Inc. is the voice of the American entrepreneur. We inspire, inform, and document the most fascinating people in business: the risk-takers, the innovators, and the ultra-driven go-getters that represent the most dynamic force in the American economy.
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January arrives with a familiar hangover. Too much food. Too much drink. Too much screen time. And suddenly social media is full of green juices, charcoal supplements, foot patches, and seven-day liver resets, all promising to purge the body of mysterious toxins and return it to a purer state. In the first episode of Strange Health, a new visualized podcast from The Conversation, hosts Katie Edwards and Dr. Dan Baumgardt put detox culture under the microscope and ask a simple question: Do we actually need to detox at all? Strange Health explores the weird, surprising, and sometimes alarming things our bodies do. Each episode takes a popular health or wellness trend, viral claim, or bodily mystery and examines what the evidence really says, with help from researchers who study this stuff for a living. Edwards, a health and medicine editor at The Conversation, and Baumgardt, a general practicioner and lecturer in health and life sciences at the University of Bristol, share a long-standing fascination with the bodys improbabilities and limits, plus a healthy skepticism for claims that sound too good to be true. This opening episode dives straight into detoxing. From juice cleanses and detox teas to charcoal pills, foot pads, and coffee enemas, Edwards and Baumgardt watch, wince, and occasionally laugh their way through some of the internets most popular detox trends. Along the way, they ask what these products claim to remove, how they supposedly work, and why feeling worse is often reframed online as a sign that a detox is working. The episode also features an interview with Trish Lalor, a liver expert from the University of Birmingham, whose message is refreshingly blunt. Your body is really set up to do it by itself, she explains. The liver, working alongside the kidneys and gut, already detoxifies the body around the clock. For most healthy people, Lalor says, there is no need for extreme interventions or pricey supplements. That does not mean everything labeled detox is harmless. Lalor explains where certain ingredients can help, where they make little difference, and where they can cause real damage if misused. Real detoxing looks less like a sachet or a foot patch and more like hydration, fiber, rest, moderation, and giving your liver time to do the job it already does remarkably well. If youre buying detox patches and supplements, then its probably your wallet that is about to be cleansed, not your liver. Strange Health is hosted by Katie Edwards and Dan Baumgardt. The executive producer is Gemma Ware, with video and sound editing by Sikander Khan. Artwork by Alice Mason. Edwards and Baumgardt talk about two social media clips in this episode, one from 30.forever on TikTok and one from velvelle_store on Instagram. Listen to Strange Health via any of the apps listed above, download it directly via our RSS feed, or find out how else to listen here. A transcript is available via the Apple Podcasts or Spotify apps. Katie Edwards is a commissioning editor for health and medicine and host of the Strange Health podcast at The Conversation. Dan Baumgardt is a senior lecturer at the School of Psychology and Neuroscience at the University of Bristol. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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