Training models with synthetic data presents both a danger and a boon to artificial intelligence (AI). While some groups have aggressively pursued the use of model-generated data to train successors for greater accuracy and generalization, others have warned about the risks posed by AI ingesting its own output. The two views are not at odds. The question is when and where things go wrong.
