# Is there "Secret Sauce'' in Large Language Model Development?

arXiv: https://arxiv.org/abs/2602.07238 · HTML: https://mlsanity.com/paper/2602.07238/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 10 references that are in the map.
- First posted on arXiv 6 Feb 2026.

## Builds on (most cited first, 8 of 8)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Scaling Laws for Autoregressive Generative Modeling](https://mlsanity.com/paper/2010.14701/) (2020)
- [Observational Scaling Laws and the Predictability of Language Model Performance](https://mlsanity.com/paper/2405.10938/) (2024)
- [Scaling Laws for Predicting Downstream Performance in LLMs](https://mlsanity.com/paper/2410.08527/) (2024)
- [Algorithmic progress in language models](https://mlsanity.com/paper/2403.05812/) (2024)
- [Scaling Laws for Deep Learning](https://mlsanity.com/paper/2108.07686/) (2021)
- [Rethinking LLM Advancement: Compute-Dependent and Independent Paths to Progress](https://mlsanity.com/paper/2505.04075/) (2025)

## Built on by (most cited first, 3 of 3)

- [Crashing Waves vs. Rising Tides: Findings on AI Automation from Thousands of Worker Evaluations of Labor Market Tasks](https://mlsanity.com/paper/2604.01363/) (2026)
- [Validity Threats for Foundation Model Research](https://mlsanity.com/paper/2606.05029/) (2026)
- [Two AI Metrics Diverged: Will it Make All the Difference?](https://mlsanity.com/paper/2607.00913/) (2026)

## Abstract (arXiv)

Do leading LLM developers possess a proprietary ``secret sauce'', or is LLM performance driven by scaling up compute? Using training and benchmark data for 809 models released between 2022 and 2025, we estimate scaling-law regressions with release-date and developer fixed effects. We find clear evidence of developer-specific efficiency advantages, but their importance depends on where models lie in the performance distribution. At the frontier, 80-90% of performance differences are explained by higher training compute, implying that scale--not proprietary technology--drives frontier advances. Away from the frontier, however, proprietary techniques and shared algorithmic progress substantially reduce the compute required to reach fixed capability thresholds. Some companies can systematically produce smaller models more efficiently. Strikingly, we also find substantial variation of model efficiency within companies; a firm can train two models with more than 40x compute efficiency difference. We also discuss the implications for AI leadership and capability diffusion.