Real data is running out. Synthetic data generation is becoming essential — but it comes with hidden risks.
The AI industry faces an inconvenient truth: it's running out of high-quality training data. Estimates suggest that all publicly available text data on the internet—approximately 300 trillion tokens—will be exhausted by major labs within the next 2–3 years at current training scales. Synthetic data generation has emerged as the primary solution.
The approach is deceptively simple: use existing AI models to generate new training data for next-generation models. OpenAI, Google, and Anthropic all confirm using synthetic data in their latest model training runs. Meta's Llama 4 was trained on a dataset that was approximately 40% synthetic.
The benefits are compelling. Synthetic data can be generated in unlimited quantities, precisely controlled for quality and diversity, and tailored to fill gaps in real-world datasets. Need more examples of medical reasoning? Generate them. Need training data in low-resource languages? Synthesise it from high-resource translations.
But the risks are equally significant. When models train on data generated by other models, errors, biases, and stylistic quirks can compound—a phenomenon researchers call 'model collapse.' A Nature paper published in late 2025 showed that models trained for multiple generations on primarily synthetic data gradually lost the ability to represent rare but important patterns in real-world data.
The emerging best practice is a 'hybrid curriculum' approach: combine high-quality real-world data with carefully filtered synthetic data, using separate validation sets to detect early signs of model collapse. Vincony's Fine-Tuning pipeline supports synthetic data generation and quality filtering as built-in features.
The synthetic data debate highlights a deeper question about the future of AI: if models increasingly learn from each other rather than from human-generated content, what happens to the diversity of thought and expression that made the original training data valuable?