The Looming Threat: How AI-Generated Data Can Undermine Future AI Models


  • Generative AI is on the rise, producing text, images, and music accessible to the public.
  • AI-generated content is increasingly prevalent online, including major websites like CNET and Gizmodo.
  • Using AI-generated data to train new AI models may inadvertently introduce errors that accumulate with each generation.
  • This phenomenon, known as “model collapse,” can render AI models unreliable and meaningless.
  • Even small amounts of AI-generated data can be toxic to the training process.
  • Larger AI models may not be immune to model collapse, and the issue primarily affects data tails with less representation.
  • The model collapse could lead to biased outputs and a loss of diversity in AI-generated content.
  • Efforts are needed to curtail biases and preserve the authenticity of AI-generated data.

Main AI News:

In the rapidly expanding realm of generative artificial intelligence (AI), there lies a potential threat that could taint the future of AI models. As AI capabilities grow, so does the availability of programs that can produce text, computer code, images, and music, rendering them accessible to the masses. The internet is already abuzz with AI-generated content, with major websites like CNET and Gizmodo incorporating texts churned out by “large language models.” However, a lurking danger emerges as AI developers scavenge the internet for data sets to train their new models to emulate human-like responses.

Evidence is amassing to support the notion that a diet of AI-generated text, even in small quantities, may eventually prove “poisonous” to the very model being trained. The ramifications of this phenomenon are not yet entirely understood, but some experts are already raising concerns. Rik Sarkar, a computer scientist at the esteemed School of Informatics at the University of Edinburgh in Scotland, foresees that it might not be an immediate problem but could evolve into a pressing consideration in the coming years.

This predicament draws an analogy to a 20th-century dilemma that arose after the detonation of the first atomic bombs. Decades of nuclear testing introduced radioactive fallout into the atmosphere, which, when incorporated into newly-made steel, led to elevated radiation levels. Similarly, in the world of generative AI, the repeated use of AI-generated data for training purposes might lead to a cascade of errors akin to the radiation-affected steel. This could result in AI models poisoning themselves, thereby compromising their reliability and usefulness.

Researchers have already witnessed AI’s poisoning in action. They observed a phenomenon called “model collapse,” where successive iterations of AI training resulted in increasingly nonsensical outputs. Even simple models attempting to separate two probability distributions were not immune to this issue. Such occurrences have raised concerns among the scientific community, including Ilia Shumailov, a machine learning researcher at the University of Oxford. He warns that model collapse renders the AI model practically meaningless.

In a study conducted by Sarkar and his colleagues in Madrid and Edinburgh, they explored a similar experiment using an AI image generator called a diffusion model. The results were disheartening, as recognizable images of flowers and birds devolved into mere blurs in the third model.

Furthermore, it was discovered that even a partially AI-generated training data set proved to be toxic. Hence, as long as a reasonable fraction of the data set relies on AI-generated content, issues are bound to arise. However, determining the exact threshold of AI-generated content that leads to problems in different types of models remains an area that requires further investigation.

The size of the model seems to play a role in the susceptibility to model collapse. While larger models might offer some resistance, researchers are cautious about placing blind faith in this idea. The data indicate that the tails of a model’s data distribution, which comprise less frequently represented elements, are most vulnerable to this issue. Consequently, model collapse could erode the diversity that characterizes human data, raising concerns about exacerbating biases against marginalized groups.

To prevent this future scenario, Shumailov emphasizes the need for explicit efforts to curb biases and preserve the authenticity of AI-generated content. As AI-generated content permeates various domains relied upon for training data, such as language models, the stakes for addressing these issues grow higher.


The proliferation of AI-generated content poses significant challenges for the AI market. The potential for model collapse and the poisoning of AI models highlights the need for cautious and ethical use of AI-generated data. Businesses in the AI sector must prioritize addressing these issues to ensure AI technologies continue to evolve positively without compromising their reliability and societal impact. A collaborative effort among industry stakeholders is crucial to striking the right balance between innovation and responsible AI development.