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We investigate the potential constraints on LLM scaling posed by theavailability of public human-generated text data. We forecast the growingdemand for training data based on current trends and estimate the total stockof public human text data. Our findings indicate that if current LLMdevelopment trends continue, models will be trained on datasets roughly equalin size to the available stock of public human text data between 2026 and 2032,or slightly earlier if models are overtrained. We explore how progress inlanguage modeling can continue when human-generated text datasets cannot bescaled any further. We argue that synthetic data generation, transfer learningfrom data-rich domains, and data efficiency improvements might support furtherprogress.

Will we run out of data? Limits of LLM scaling based on human-generated data