--- title: "AI Appears to Be Slowly Killing Itself" description: "AI-generated content flooding the web. That could be a problem for AI models, which are widely trained via web-scraped data." date: "2024-08-27" modified: "2024-08-27" authors: - name: "Maggie Harrison Dupré" job_title: "Senior Staff Writer" link: "https://futurism.com/authors/mharrison" url: "https://futurism.com/ai-slowly-killing-itself" categories: - "Artificial Intelligence" tags: - "ai" - "ai-generated content" - "data" - "OpenAI" --- # AI Appears to Be Slowly Killing Itself ![AI-generated content flooding the web. That could be a problem for AI models, which are widely trained via web-scraped data.]() *\Image: Getty / Futurism\* AI-generated text and imagery is flooding the web — a trend that, ironically, could be a huge problem for generative AI models. As [Aatish Bhatia writes for *The New York Times*](), a growing pile of research shows that training generative AI models on AI-generated content causes models to erode. In short, training on AI content causes a flattening cycle similar to inbreeding; the AI researcher Jathan Sadowski last year [dubbed the phenomenon]() as "Habsburg AI," a reference to Europe's famously inbred royal family. And per the *NYT*, the rising tide of AI content on the web might make it much more difficult to avoid this flattening effect. AI models are ridiculously data-hungry, and AI companies have relied on vast troves of data scraped from the web in order to train the ravenous programs. As it stands, though, neither AI companies nor their users are required to put AI disclosures or watermarks on the AI content they generate — making it that much harder for AI makers to keep synthetic content out of AI training sets. "The web is becoming increasingly a dangerous place to look for your data," Rice University graduate student Sina Alemohammad, who [coauthored a]() [2023 paper]() that coined the term "MAD" — short for "Model Autophagy Disorder" — to describe the effects of AI self-consumption, told the *NYT*. We [interviewed Alemohammad last year](), back when little attention was being paid to AI-generated data polluting AI datasets, so it's been interesting to watch the issue gain attention. One admittedly very funny example of the impacts of AI inbreeding flagged by the *NYT* was taken from a new study, [published last month in the journal ]()*[Nature]().* The researchers, an international cohort of scientists based in the UK and Canada, first asked AI models to fill in text for the following sentence: "To cook a turkey for Thanksgiving, you…" The first output was normal. But by just the fourth iteration, the model was spouting [complete gibberish](): "To cook a turkey for Thanksgiving, you need to know what you are going to do with your life if you don 't know what you are going to do with your life if you don 't know what you are going to do with your life..." But gibberish isn't the only possible negative side effect of AI cannibalism. The MAD study, which focused on image models, showed that feeding AI outputs of faux human headshots quickly caused a bizarre convergence of facial features; though the researchers started with a diverse set of AI-generated faces, by the fourth generation cycle — is that a magic number in AI, for some reason? — nearly every face looked the same. Given that [algorithmic bias]() is already a huge problem, the risk that accidental ingesting of too much AI content might contribute to less diversity in outputs looms large. High-quality, human-made data — and *lots* of it — has been central to recent advancements in existing generative AI tech. But with AI-generated content muddying digital waters and no reliable way of determining real from fake, AI companies could soon find themselves [hitting a dangerous wall](). **More on AI inbreeding:** [*When AI Is Trained on AI-Generated Data, Strange Things Start to Happen*]() ## Author At Futurism, I've reported extensively on the rise of AI as a cultural and business force shaping the media industry, and more broadly how those dynamics are changing how we all consume and share information and relate to one another. I'm also fascinated by public health policy and ethics, the role of emerging tech in politics and governance — and the powerful people and forces at those intersections — climate change, and the environment. My investigation on Sports Illustrated's use of AI-generated authors with fictional biographies won a 2024 Mirror Award for "Best Story on Media Coverage of Artificial Intelligence in Journalism and the Media" from Syracuse University's SI Newhouse School, I contributed to Niemen Lab's 2025 Predictions for Journalism series, and I've discussed my work for Futurism during appearances on NPR, CNN, the BBC, the CBC, and more. I grew up in rural Pennsylvania and attended the University of Massachusetts Amherst, where I played Division I field hockey for the Minutewomen as a midfielder. Since then, I've lived in New Orleans, Louisiana and Manhattan, New York. I spend my free time running, reading, perusing archival fashion, and searching for the world’s best negroni. I also have a debonair tuxedo cat, Westley, who's named after "The Princess Bride." ### Author social links [Bluesky]()