{"data":{"slug":"scaling-laws","etype":"concept","name":"Scaling Laws","cat":"Training & Inference","subtitle":"The empirical curves that justify the capex on this site.","summary":"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.","attrs":null,"kf":null,"aliases":["scaling law","Chinchilla"],"ticker":null,"exchange":null,"hq":null,"founded":null,"website":null,"status":null,"region":null,"asof":"2026-01-15","sources":[{"url":"https://arxiv.org/abs/2001.08361","label":"Kaplan scaling laws"}]},"meta":{"found":true,"count":1,"served_from":"site cache (no live upstream calls)","as_of":"2026-10-14T05:34:18+00:00","disclaimer":"Research and information only — not investment advice. Structured records carry basis labels (vendor spec / reported / estimate); market quotes are delayed/indicative.","attribution":"Records compiled from vendor documentation, official filings and reputable public reporting; market quotes via financial data providers. See /data-sources."}}