The paper "Is there 'Secret Sauce' in Large Language Model Development?" (February 2026) investigates whether the rapid progress in Large Language Models (LLMs) is driven by scaling up compute or by proprietary developer techniques. Analyzing data from 809 models released between 2022 and 2025, the researchers decomposed LLM performance into four factors: scaling (compute), shared algorithmic progress, developer-specific "secret sauce," and model-specific optimizations,.

Key findings from the study include:

• Scale Dominates the Frontier: At the performance frontier, 80%–90% of performance differences are explained by training compute,. This suggests that "secret sauce" plays only a modest role in pushing the absolute limits of AI capabilities; instead, frontier advances are primarily driven by massive increases in scale,.
• The Role of "Secret Sauce": While less critical at the frontier, proprietary techniques are vital for models below that threshold,. Some developers are up to 61 times more compute-efficient than others, allowing them to produce smaller, cheaper models with relatively high performance,,.
• Shared Algorithmic Progress: Broad technological gains across the field increased effective compute by a factor of 7.5x between early 2023 and late 2024,.
• Intra-Company Variation: Efficiency varies significantly even within a single company’s lineup; one firm can produce two models with over a 40x difference in compute efficiency,.

The authors conclude that sustained leadership in frontier AI requires continued access to massive compute resources,. However, the "secret sauce" of technical progress is effectively democratizing AI by enabling the creation of high-performing, low-cost models for broader use.