What is synthetic data?

Synthetic data is data generated artificially, by a statistical model, a simulation or another AI model, rather than collected from real events. It is not a copy of real data: it reproduces its statistical properties (distributions, correlations, edge cases) without containing real information about specific people or events, which makes it useful wherever real data is scarce, imbalanced or too sensitive to share. There is more than one concrete use case: generating a dataset that replicates the properties of real customer data without exposing personal information, enriching the training set when the events a model needs to recognize (a fraud case, a rare failure) are too scarce in real data, or simulating robotic environments, as NVIDIA Cosmos does, at a fraction of the cost of collecting real physical demonstrations. The limit worth remembering is that it inherits the biases of whatever generator produced it, so it should always be validated against an independent sample of real data before being trusted.

See also: Data quality · How fit your data is for its intended use: complete, correct, fresh and consistent across systems. Measured, not declared.

Three concrete uses, not just one

The most cited case is privacy: generating a synthetic dataset that replicates the properties of real customer data lets you develop and test without exposing personal information, a topic that directly intersects with data governance. The second case is class balancing: when the events a model needs to learn to recognize (a fraud case, a rare failure) are very few in real data, synthetic data lets you enrich the training set with plausible examples without waiting for the real event to occur a thousand more times. The third case, the one closest to the Physical AI cluster, is robotics simulation: platforms like NVIDIA Cosmos generate synthetic environments and scenarios to train a VLA model or a world model, at a fraction of the cost of collecting an equivalent set of real physical demonstrations.

The limit worth keeping in mind

Synthetic data is not a free lunch: it inherits the biases and blind spots of the generator that produced it. If the model or simulation generating the synthetic data has a skewed or incomplete understanding of reality, the synthetic dataset amplifies it instead of correcting it, and a model trained only on it risks performing well in the lab and poorly in the field. The honest practice is to use it to complement real data, not replace it, and always validate the final result against an independent sample of real data.