The internet is running out of high-quality human writing. To train larger models, research labs are generating synthetic data—text, code, and reasoning steps produced by existing AI models. This creates a feedback loop that could redefine machine learning.
The primary risk of synthetic training is model collapse, where statistical errors and hallucinations compound over generations, eventually degrading the model's output. To prevent this, labs must implement strict filtering and validation steps.
When done correctly, synthetic data can actually outperform raw internet scrapes. By generating structured, mathematically verified problem sets and clean code samples, synthetic pipelines provide high-density training tokens that teach reasoning rather than mere imitation.
