A new study published in Nature highlights the potential for 'model collapse' in AI systems when trained on data generated by their own outputs, leading to degradation in performance and nonsensical results. Researchers from the University of Oxford found that as AI models increasingly rely on AI-generated datasets, they may lose the ability to produce coherent content, creating a recursive loop of degrading quality. This phenomenon was illustrated with examples, including one that transformed a text about medieval architecture into irrelevant mentions of jackrabbits. The researchers emphasized the importance of using reliable, curated datasets to prevent this collapse, warning that if current trends continue, genuine information may become increasingly difficult to find. While the Digital Transformation Agency recognizes the productivity potential of AI in workplaces, it also cautions that the current applications are still in early stages and may not meet expectations. This underscores the need for careful implementation and oversight in AI training practices to mitigate risks.