  We investigate the potential constraints on LLM scaling posed by the
availability of public human-generated text data. We forecast the growing
demand for training data based on current trends and estimate the total stock
of public human text data. Our findings indicate that if current LLM
development trends continue, models will be trained on datasets roughly equal
in 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 in
language modeling can continue when human-generated text datasets cannot be
scaled any further. We argue that synthetic data generation, transfer learning
from data-rich domains, and data efficiency improvements might support further
progress.
