Synthetic Data
Synthetic data is training or evaluation data generated by a model rather than collected from the real world. It's used to cover cases real data is missing, scarce, expensive, or too sensitive to use — and increasingly to train models when high-quality human data runs short.
Also known as: synthetic training data, generated data
Real data is the bottleneck in most AI work: it’s expensive to label, scarce for rare cases, and often can’t be used at all for privacy reasons. Synthetic data sidesteps that by generating examples — a stronger model writing training pairs, simulated edge cases, augmented variations — to fill the gaps. DeepSeek’s use of reinforcement learning to auto-generate step-by-step reasoning data is a high-profile example of synthetic data shaping a frontier model.
The leverage is real, and so is the risk. Synthetic data inherits the biases and blind spots of the model that made it, and training a model heavily on another model’s output can amplify errors or narrow diversity. The practical stance builders take: use it to augment and cover gaps, validate it against real data and good evaluation, and don’t mistake volume for quality.
From the conversation
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Beyond Transformers: How Liquid AI Is Rethinking LLM Architecture | Maxime Labonne - Why LLMs Are Plausibility Engines, Not Truth Engines | Dan Klein
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- First Code, Then AGI: Software’s Event Horizon with Poolside Founders Jason Warner & Eiso Kant
- The Critical Infrastructure Behind the AI Boom | Cisco CPO Jeetu Patel
- Architecting Reliable Agentic AI | Cisco’s Giovanna Carofiglio on the AGNTCY Collective
- Inside IBM's watsonx: Building Enterprise AI That Ships | Dr. Maryam Ashoori
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How Superhuman Built AI Into a 100ms Product | Loïc Houssier