When real data runs out, can fake data fill in? A new framework says: carefully

Mixing synthetic data with real data is like blending a concentrate with juice — the ratio matters enormously, or you'll either water things down or make them too strong.

This means we can now safely use AI-generated training data in practice without sacrificing accuracy, which opens doors for fields where real data is expensive or hard to get.