What Is Synthetic Data, and Why Do AI Labs Use It to Train Models?
Some of a model's training data isn't written by humans at all — it's generated by other AI models. Here's why that's become standard practice.
Data generated by AI, for AI
Synthetic training data is content generated by an existing AI model, specifically to train another model — rather than collected from real-world human-written sources. It's increasingly used to fill gaps in real data, especially for tasks where high-quality human-written examples are scarce or expensive to produce at scale.
Why labs turn to it
High-quality human-written training data — especially for narrow domains like advanced math or code — is limited and expensive to produce. A capable model can generate large volumes of plausible, structured examples for a target task far more cheaply, which then get used (often after filtering) to train the next model.
The risk to watch for
Training too heavily on synthetic data, especially data generated by the same model family repeatedly, risks amplifying that model's existing quirks and errors rather than correcting them — an effect sometimes called model collapse if left unchecked. Careful filtering and mixing with real data is standard practice specifically to guard against this.
Frequently Asked Questions
What is synthetic data in AI training?
Training content generated by an AI model rather than collected from real-world human-written sources, used to supplement real data, especially for tasks where high-quality examples are scarce.
Is synthetic training data risky?
It can be if overused — training too heavily on AI-generated data, especially from the same model family, risks amplifying existing errors rather than correcting them. Careful filtering and mixing with real data mitigates this.