Public, high-quality human-generated text is a genuinely finite resource, and some researchers estimate the industry will exhaust the readily available stock of it within the next several years. Synthetic data, examples generated by another model rather than collected from the real world, is one response: it can fill gaps, balance underrepresented scenarios, or provide training examples for situations too rare or too sensitive to source directly (rare medical conditions, security incidents, protected data).
It’s not a clean substitute for real data, though. Training heavily on model-generated data can cause a model to drift toward its own patterns and errors rather than the real-world distribution it’s supposed to represent, a degradation sometimes called model collapse. Results on this are genuinely mixed in practice, not a solved problem.
The practical implication for a buyer is a diligence question: if a vendor’s model is heavily trained on synthetic data, ask what was done to validate it still reflects real-world behaviour, not just internally consistent patterns the generating model happened to produce.