Synthetic Data

Synthetic data is data generated by an algorithm, increasingly by another AI model, rather than collected from real people or events. It is designed to resemble real data closely enough to train or test a model. For language models, it usually means text written by a model: rewritten web pages, generated textbook-style explanations, question-and-answer pairs, step-by-step solutions or simulated conversations.

The idea is older than LLMs. Statisticians and data teams have long generated synthetic tabular records to share data without exposing real people, and autonomous vehicle and robotics teams train on simulated scenes. What changed is scale and role. Synthetic text is now a core ingredient in training language models at every stage:

• Pretraining: some models mix synthetic text into their base training data. Microsoft's Phi-4 technical report states that synthetic data made up 40% of its pretraining mix, with another 15% from model-rewritten web pages (Phi-4 technical report).
• Instruction tuning and alignment: models are taught to follow instructions using generated example dialogues, and preference data is often produced or labeled by AI judges rather than people.
• Reasoning: labs generate large numbers of math and coding problems with checkable answers, then reward models for solving them, the approach behind many reasoning models.
• Distillation: a large model produces answers that a smaller model learns to imitate, a common route to capable small language models.
• Evaluation: generated test sets probe behaviors that real data covers poorly.

The main risk is training on model output without care. In a 2024 paper in Nature, Shumailov and colleagues showed that models trained recursively on data generated by earlier models lose the rare, low-probability parts of the original distribution and drift toward bland, repetitive output, an effect they called model collapse. Later work suggests the risk is manageable when synthetic data is filtered, verified and mixed with fresh human data rather than replacing it. The Nature authors themselves concluded that data from genuine human interactions becomes more valuable as AI-generated text spreads across the web. Other concerns include errors copied from the generating model, narrower stylistic range, and weak factual grounding when generated text is not checked against sources.

Why this matters for GEO: synthetic data improves how models reason and write, but it adds little new knowledge about the world. Facts about specific companies, products, prices and events still come from human-created sources, either in pretraining data or through live retrieval at answer time. As more of the web fills with AI-generated text that restates existing material, original information becomes the scarce input: first-party data, documented product details, expert analysis and real customer evidence. Pages offering information gain, facts not already repeated across a hundred other pages, are the kind of source both training pipelines and retrieval systems have reason to favor. Mass-produced AI copy that only rephrases competitors' pages adds to the noise rather than standing out. Related: AI training data, training data optimization, AI content generation and data privacy in AI.