# AI scaling laws

Record: term-scaling-laws · Type: term · Edition: 0.20.0 · Evidence cutoff: 2026-09-15

[Read in the atlas](https://theaiatlas.org/ideas/scaling-laws/) · [Complete evidence](https://theaiatlas.org/evidence.html#idea-scaling-laws) · [JSON](https://theaiatlas.org/records/term-scaling-laws.json) · [Pinned complete dataset](https://theaiatlas.org/editions/e74392d479c0e7da8636a7d6a0454d03510df86ffca9931eb86babd952665ca5/data.json)

Dataset pointer: `/glossary/139`. Reviewed: 2026-09-15.

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## Publication dates and source age

Sources are over 18 months old.

Newest dated source: 2022-03-29. Assessed at this edition’s evidence cutoff: 2026-09-15. 18-month boundary: 2025-03-15.

Publication age does not establish validity or a new source-reading date. Unknown dates and month/year precision remain explicit in the JSON record.

## /summary

Measured patterns linking model size, training data and computing resources to performance. Each pattern concerns a particular measurement and training setup.

Claim: claim-term-scaling-laws-1a5192ba3d7825ca26b24a72. Annotation: synthesis.

[scaling-kaplan](https://arxiv.org/abs/2001.08361) · [scaling-chinchilla](https://arxiv.org/abs/2203.15556)

## /definition

Researchers train different-sized models and fit equations to the results. In language-model research, a common measure is prediction loss: how poorly the model predicts the next text piece.

Claim: claim-term-scaling-laws-1e4c26398ee834b2e16dc7b8. Annotation: synthesis.

[scaling-kaplan](https://arxiv.org/abs/2001.08361) · [scaling-chinchilla](https://arxiv.org/abs/2203.15556)

## /placement

Map context: these studies inform expectations about capability growth and resource use.

Claim: claim-term-scaling-laws-25f52a21ad9f2e54a62ed1b2. Annotation: editorial.

[scaling-kaplan](https://arxiv.org/abs/2001.08361) · [scaling-chinchilla](https://arxiv.org/abs/2203.15556)

## /distinction

Larger is not the only choice. Hoffmann and coauthors found better results by balancing model size with more training data. Extrapolating a measured trend to untested scales adds an assumption.

Claim: claim-term-scaling-laws-09422ce4d5c74cb753a9bb98. Annotation: synthesis.

[scaling-kaplan](https://arxiv.org/abs/2001.08361) · [scaling-chinchilla](https://arxiv.org/abs/2203.15556)

## Source provenance

### scaling-kaplan

[Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361)

Jared Kaplan and coauthors / arXiv · First-hand source (primary) · Published: 2020-01-23 · Material last read: 2026-09-15 · Verification: read

Read scope is described in the source note.

Read abstract and submission record. Empirical relationships concern language-model prediction loss and training resources, not an AGI date.

No archive check recorded.

### scaling-chinchilla

[Training Compute-Optimal Large Language Models](https://arxiv.org/abs/2203.15556)

Jordan Hoffmann and coauthors / arXiv · First-hand source (primary) · Published: 2022-03-29 · Material last read: 2026-09-15 · Verification: read

Read scope is described in the source note.

Read abstract and submission record. Tests balance parameter count and training tokens under a fixed compute budget. Results are scoped to the studied setups.

No archive check recorded.