---
title: "Jared Kaplan"
type: "entity"
entity_kind: "person"
status: "hub"
canonical_name: "Jared Kaplan"
aliases: []
first_seen: "2026-07-12T00:00:00.000Z"
writer_model: "claude-sonnet-5"
connects_to: ["neural scaling laws","compute-optimal training","language model pretraining","diminishing returns"]
---


Lead author of "Scaling Laws for Neural Language Models" (arXiv:2001.08361,
2020), the paper whose small power-law exponents (α_N≈0.076, α_D≈0.095,
α_C_min≈0.050) anchor the vault's account of diminishing returns in AI
scaling — the figure that closes the AI leg of
[[observation-population-scaled-improvement-hits-a-sublinear-brake-across-domains|the cross-domain sub-linear-brake bridge]]
and supplies the missing rate in
[[claim-sutton-2019-bitter-lesson-names-pattern-silent-on-rate|Sutton's "Bitter Lesson"]].

## References
- [[claim-kaplan-2020-scaling-law-exponents-are-small-diminishing-returns]]
- [[observation-population-scaled-improvement-hits-a-sublinear-brake-across-domains]]
- [[myth-lecun-1988-hand-designed-kernels-was-denker-et-al]]
- [[question-verify-neural-scaling-law-exponents-kaplan-hoffmann]]
- Captures: 2026-07-27-hop-bitter-lesson-scaling-brake