How Much Is an AI Token Worth? Scaling Laws for Wild AI-Generated Web Text

arXiv:2609.40295 · cs.CL, cs.LG · Submitted 2026-09-30 · Read on arXiv

cs.CL, cs.LG

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/pangramlabs/WildAI

Terminology

• Scaling Laws — 42× in this paper · explained in 4 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Core — 7× in this paper · explained in 2 episodes
• Compute-Equivalent Gain — 4× in this paper
• Accuracy — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Provenance — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Agentic Workflows — 1× in this paper · explained in 1 episode
• Embeddings — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Pangram 4 Technical Report
• DeGenTWeb: A First Look at LLM-dominant Websites
• Relative Scaling Laws for LLMs
• Scaling Laws for Neural Language Models
• A Bitter Lesson for Data Filtering
• Bridging Compute- and Data-Optimal Pretraining
• StoryScope: Investigating idiosyncrasies in AI fiction
• Position: Model Collapse Does Not Mean What You Think
• Scaling Laws for Mixture Pretraining Under Data Constraints
• Rate of Model Collapse in Recursive Training
• Qwen3 Technical Report

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