Scaling Laws
The empirical curves that justify the capex on this site.
You're looking at a concept explainer: what this term means in plain English, and the records it ties together.
Scaling laws (Kaplan 2020; Chinchilla 2022) show model loss falling predictably as compute, parameters and data grow together — the smooth curves that convinced investors trillion-dollar infrastructure was rational. Chinchilla corrected the recipe (more data per parameter); test-time compute added a second axis; the open question — whether pre-training returns are flattening — is the trillion-dollar debate, with Ilya Sutskever's 'age of scaling is over' remarks its sharpest statement.
How this connects to the rest of the stack
This record's links at a glance — click a group to jump to it.
Based on
Sources
Record reviewed as of 2026-01-15 — see Methodology.