In the rapidly evolving world of AI, data is king—but what happens when the data we rely on is synthetic? While data augmentation offers a promising solution to the challenges of collecting real-world examples, it comes with hidden dangers. From the risk of model collapse to the inability to capture the nuances of human language, synthetic data can lead to fragile models that struggle in real-world scenarios. Discover why balancing synthetic and authentic data is crucial for building robust AI systems, and learn how our approach to cyberbullying detection exemplifies this delicate equilibrium.