Across 800 pretrained models, wild AI web text raises loss once data is plentiful, and a 31.1% AI share costs 1.6x the compute
Using EditLens and Pangram to label Common Crawl web data from 2021 to 2026, the authors find that 27.5% of tokens passing FineWeb quality filtering in June 2026 are AI-generated, rising to 31.1% in August, and by pretraining 800 models from 19.9M to 973M parameters while varying the ratio of added AI to human tokens they fit a new scaling law with separate benefit and harm terms: AI tokens lower loss for data-starved models before saturating and reversing into harm, raise loss almost immediately for Chinchilla-optimal models, and the law predicts held-out sizes with lower error than eleven existing laws, implying that training at August 2026's AI share takes 1.6x the compute.