When real data is scarce, expensive to collect, or too sensitive to use, synthetic data offers an alternative: data generated artificially rather than collected from the real world. A model might generate thousands of realistic medical images to supplement a small clinical dataset. A simulator might produce driving scenarios for autonomous vehicle training. A language model might create example customer queries to train a chatbot. The appeal is obvious - you can produce as much data as you need, with perfect labels, covering exactly the scenarios you care about, without any privacy concerns. The reality is more nuanced. Synthetic data is only useful if it faithfully represents the real-world patterns your model needs to learn. If your generated data diverges from reality in subtle ways, your model learns those divergences and performs poorly on actual data. There is a genuine risk of creating a feedback loop where AI-generated data trains the next AI model, gradually amplifying small errors or biases with each generation. The best results typically come from mixing synthetic and real data, using the synthetic examples to supplement rather than replace real-world observations. Done carefully, synthetic data is a powerful tool. Done carelessly, it teaches your model a convincing but fictional version of reality.