Training a modern machine learning model requires an enormous amount of data, and the internet’s supply of high-quality, freely usable text, images, and video is not infinite. It’s also increasingly encumbered by copyright disputes, licensing restrictions, and platforms locking down their content. This scarcity has pushed AI labs toward a different source: data generated by other models. Synthetic data, once a niche technique for filling gaps in small datasets, has become a core part of how frontier models are trained.

What Synthetic Data Actually Is

Synthetic data is any training example that was created algorithmically rather than collected from the real world. In practice this mostly means using an existing large language model to generate text, question-answer pairs, code samples, or reasoning traces that then get used to train or fine-tune another model. The technique extends to other domains too: simulated sensor data for training self-driving systems, procedurally generated images for computer vision, and synthetic financial transactions for fraud detection models where real data is too sensitive to share.

The appeal is straightforward. Synthetic data is cheap to produce at scale, can be generated on demand for underrepresented scenarios, and avoids many of the privacy and copyright issues tied to scraped or licensed content. If you need a million examples of a rare edge case, a model can generate them far faster than humans can find or label them.

Where It’s Already Load-Bearing

Synthetic data plays an outsized role in a few specific areas of model training. Reasoning-focused models rely heavily on synthetic chains of thought, where a larger or more capable model generates step-by-step reasoning traces that a smaller model then learns to imitate. This is a form of distillation, and it’s one of the main reasons smaller open models have closed the gap with larger ones so quickly.

Instruction tuning and alignment work also lean on synthetic data. Rather than paying humans to write thousands of example conversations, labs generate candidate responses with one model and use another model, or human raters, to select or rank the best ones. This bootstrapping approach is far cheaper than fully human-authored datasets and can be iterated on much faster.

Code generation is another strong fit. Code can be automatically checked for correctness by running it, which gives synthetic code data a built-in verification signal that text data usually lacks. This is part of why coding models have improved so quickly relative to general-purpose language models.

The Model Collapse Problem

The obvious risk is feedback loops. If models increasingly train on data produced by earlier models, errors, biases, and stylistic quirks can compound across generations rather than get corrected. Researchers refer to this failure mode as model collapse: a gradual narrowing of the diversity and quality of generated output as synthetic data displaces real-world data over successive training rounds.

In practice, labs try to mitigate this by keeping a mix of real and synthetic data, filtering synthetic examples for quality and diversity, and using verification signals where possible, such as unit tests for code or symbolic checks for math. Synthetic data generated for narrow, verifiable tasks tends to be much safer than synthetic data meant to stand in for open-ended human knowledge or opinion.

Why This Trend Will Keep Growing

As the web’s supply of untapped, high-quality data continues to shrink relative to the appetite of ever-larger training runs, synthetic data isn’t optional anymore, it’s a structural necessity. The more interesting question going forward isn’t whether labs will use synthetic data, but how well they can verify and curate it. The labs that build the best filtering, verification, and diversity-preserving pipelines for synthetic data will have a real advantage, because raw generation capacity is now a commodity that almost every serious AI lab has access to.