Quick answer

Synthetic data is artificially generated data — text, images, code, or structured records — created specifically to train or fine-tune AI models, used when real-world data is too scarce, too expensive to label, or too sensitive to use directly. A common approach: use a strong existing model to generate thousands of example question-answer pairs, then train a smaller or specialised model on that generated set. It has become central to frontier training since 2024, partly because the internet's supply of fresh, high-quality real text is running low relative to how much data ever-larger models want.

Training a frontier AI model takes an enormous amount of data. For years that meant scraping the open internet — but the highest-quality, easily accessible text has largely already been scraped, repeatedly, by every major lab. Synthetic data is one of the main answers to "what do we train on next."

How it actually gets made

  • A strong existing model generates large volumes of example content — questions and answers, code and explanations, conversations
  • That generated content is filtered and scored for quality before being used
  • A new (often smaller or more specialised) model is trained or fine-tuned on the resulting synthetic dataset
  • This is especially common for teaching a model a specific skill — like following instructions well, or reasoning through a particular kind of problem

Where it is used

Synthetic data shows up heavily in domains where real examples are rare or sensitive — medical scenarios, financial fraud patterns, rare edge cases in self-driving — and in the "instruction tuning" phase that teaches a raw base model to actually follow user requests helpfully, which relies heavily on generated example conversations.

The risk is "model collapse" — training a model too heavily on other AI's output, rather than real-world data, can amplify small errors and gradually homogenise outputs across generations of models.

The honest trade-off

Used carefully, alongside real data rather than instead of it, synthetic data is now a standard, useful part of how frontier labs train models. Used carelessly — training almost entirely on a previous model's output, generation after generation — it risks quietly degrading quality in ways that are hard to detect until it's already happened.

Related reading

What Is AI Model Distillation? What Is Fine-Tuning in AI?

Bottom line

Synthetic data is neither a shortcut nor a red flag by itself — it is a now-standard tool that works well mixed with real data and carries real risk when relied on too heavily. Most frontier labs in 2026 treat it as one ingredient among several, not a replacement for real-world data.