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concepts → Quantity

Quantity

Scaling exponent

frontieras of 2026-092 connections

the small number in the power law — the most valuable empirical constant in the field

Where it sits

The prism has six jobs across and eight layers down. Its primary cell is measure × L1. Hatched cells cannot exist — a GPU does not learn, an institution does not infer.

Evidence: benchmark.

What must come first — and what it unlocks

Left to right is reading order, derived from the prerequisite_of edges. Nothing here is hand-ordered: the diagram is the graph.

nothing comes first — this is a starting pointScaling exponentnothing depends on it yet — a leaf in the reading order

What kind of thing it is — and what it is made of

It is part of: Scaling law.

Where to read it

The chapter that introduces it, and any chapter that uses it again.

16Why Bigger Worksact IV · The Learning Floor

Where it comes from

paperScaling Laws for Neural Language ModelsJared Kaplan, et al. · 2020
paperExplaining Neural Scaling LawsBahri, Dyer, Kaplan, Lee, Sharma · 2021

Every connection

All 2 edges touching this node, grouped by relation family — the sections above are highlights from this list. Colours match the relation families inthe atlas.

Structure · 1
is part ofScaling lawResult
Flow · 1
is used byThe scaling debateOpenQuestion

This page is a projection of one node in src/data/concepts.ts. It has no prose file of its own — 521 declared edges produce all 340 of these pages. Edit an edge and both endpoints change.