Synthetic data is an appealing answer to a basic AI constraint: useful real-world data can be scarce, sensitive, expensive, biased, or slow to label. A generator can produce additional examples at enormous scale, target rare cases, and attach labels automatically. But artificial records do not create knowledge from nothing. They encode assumptions from source data, models, rules, and prompts—and can reproduce their errors with industrial efficiency.
The term covers several practices. A company might simulate sensor readings from a physical model, render street scenes for autonomous-driving tests, generate question-and-answer pairs with a language model, or release statistically modeled records in place of confidential microdata. The U.S. Census Bureau defines synthetic data in confidentiality applications as modeled statistical outputs that resemble the format of protected data. That purpose is different from generating reasoning exercises for a language model, even though both create artificial examples.
Scale and coverage are the promise
In domains governed by clear rules, synthetic data can explore a combinatorial space more cheaply than people can label it. Google DeepMind's AlphaGeometry began with one billion random geometric diagrams, derived their relationships, and filtered the result into 100 million unique training examples. Trained without human demonstrations, the system solved 25 of 30 Olympiad geometry problems in its evaluation. The project description also notes a crucial advantage: computer-checkable proofs made correctness easier to verify than it would be for open-ended prose.
Language-model development offers another case. Microsoft's 14-billion-parameter Phi-4 used synthetic data for the bulk of its training data, including multi-agent prompting, self-revision, instruction reversal, rejection sampling, and preference optimization. Microsoft reports that Phi-4 exceeded its GPT-4 teacher on selected STEM question-answering benchmarks. That result does not prove a small model is generally superior, but the technical report demonstrates how curated artificial examples can transfer and organize capabilities more efficiently than indiscriminate web-scale collection.
Synthetic sets can also target cases that are rare but important: an unusual equipment failure, a low-frequency fraud pattern, or a medical image characteristic that is underrepresented in available data. Developers can vary controlled attributes and know the intended label. This makes synthetic data especially useful for pretraining, testing pipelines, stress tests, and supplementing—not automatically replacing—observed data.
Privacy is possible, not automatic
Replacing names with fictional records can reduce direct exposure, but resemblance is not a privacy proof. A model may memorize a rare source record, reveal whether someone was in the original dataset, or preserve a combination of attributes that makes a person identifiable. The closer artificial data tracks sensitive originals, the more useful it may be—and the more carefully disclosure risk must be tested.
NIST's 2025 differential-privacy guidance states that synthetic-data techniques without differential privacy generally offer only informal guarantees. It also warns that synthetic generation adds uncertainty, can reduce accuracy for subpopulations, and can propagate algorithmic bias. Differential privacy provides a mathematical bound on how much any individual's record can affect an output, but it introduces its own privacy–utility tradeoff and must be implemented correctly.
Generated errors can compound
Synthetic data reflects the generator's view of the world. If a teacher model systematically mishandles a dialect, a diagnosis, or a long-tail scenario, generating more examples from that model can turn the weakness into a larger, cleaner-looking dataset. Balancing categories does not help if the examples within a category are unrealistic.
Recursive training creates a sharper hazard. In a 2024 Nature study using theoretical models and experiments, researchers found that repeatedly training on model-generated data caused low-probability parts of the original distribution to disappear. Their conclusion was that “model collapse is universal among generative models” under the recursive conditions studied. The paper does not show that any use of synthetic data triggers collapse: preserving original data reduced degradation in its language-model experiments. It shows why provenance and mixture design matter.
Bias can compound too. A generator fitted to an unrepresentative population will usually reproduce that population's gaps unless developers deliberately model and validate alternatives. Artificial diversity should therefore be measured against real outcomes, not judged by surface variety.
Validation determines value
A credible synthetic-data program begins with a declared purpose. Data sufficient for software testing may be unsuitable for estimating disease prevalence. Developers should keep real holdout sets; measure performance by subgroup and on rare events; test duplicates, leakage, and membership inference; document generators and source datasets; and track the synthetic share of each training mixture.
For rule-based domains, automated verification can filter invalid examples. For subjective or open-world tasks, domain experts and empirical comparisons remain essential. Teams should also separate privacy evaluation from utility evaluation: a dataset can be private but inaccurate, realistic but disclosive, or poor on both dimensions.
Synthetic data is best treated as an instrument, not a substitute for observation. It can make models more capable and datasets safer to share when its generation process is testable and its limits are explicit. Without those disciplines, artificial abundance can conceal a shortage of trustworthy evidence.