What it means
Once high-quality human text became a limiting resource, labs turned to generating training data with models. Synthetic data is now used heavily: to create examples in domains where real data is scarce, to produce worked reasoning traces, and to construct problems with verifiable answers that a model can be trained against.
It works better than intuition suggests, because generation can be filtered. A model can produce a million candidate math problems, and only those with verified-correct solutions are kept — so the training signal is higher quality than the raw generation.
The risk is model collapse: train repeatedly on unfiltered model output and the distribution narrows, losing the tails where unusual but real cases live. Verification and mixing with genuine human data are what keep this from happening in practice.
Synthetic data also has a privacy use — generating realistic but non-real records to develop against without exposing actual customer data.
Why it matters
The "we're running out of training data" concern is much weaker than it was, because a large share of new training data is now manufactured. For businesses, the privacy application is the immediately usable one.
In practice
If you generate synthetic training or test data, keep a verification step — unverified model output as training input is exactly the path to degraded, narrowed behavior.