Scaling Laws Decoded: How Model Size and Data Shape AI Performance

A new blog post by Lilian Weng breaks down neural scaling laws, explaining that AI performance improves predictably with larger models and more data. It contrasts the Kaplan (2020) view—prioritize model size—with the Chinchilla (2022) finding that model and data should scale equally. The post warns that scaling laws are empirical, not physical, and extrapolation can be misleading.