A new study by researchers at the Massachusetts Institute of Technology suggests that the rapid improvement of large language models is driven primarily by access to massive computing power rather than secret proprietary techniques developed by individual AI companies.
The paper, titled Is there “Secret Sauce” in Large Language Model Development?, analyzes 809 large language models released between October 2022 and March 2025 to understand what factors are responsible for improvements in AI capabilities.
The researchers examined benchmark performance and training data for the models and attempted to separate the sources of progress into four components: the amount of training compute used, shared algorithmic progress across the industry, developer-specific techniques, and model-specific design choices.
The study begins from the observation that AI systems have improved at an extraordinary pace in recent years.
“Large language models (LLMs) have experienced a period of rapid progress and benchmark scores have climbed at an astonishing rate,” the authors write.
One of the central questions behind that progress is whether leading AI companies possess a technological “secret sauce” that gives them a sustained advantage.
The analysis found some evidence of company-specific advantages, but their overall impact appears limited when examining the most advanced models. According to the researchers, “14-18 percent of LLM performance differences are explained by company-specific effects,” indicating that proprietary engineering techniques do contribute to performance improvements.
However, the study concludes that the dominant factor behind frontier-level AI performance is simply scale – the massive computing resources used to train the largest models.
At the leading edge of AI development, the researchers estimate that “80-90 percent of frontier model performance is a consequence of these models’ large and increasing compute.”
The results suggest that while engineering improvements and algorithmic innovations do matter, they are overshadowed by the dramatic increase in computing power devoted to training modern models.
Over the period studied, the training compute used for the most powerful models grew by roughly a factor of 5,000, far exceeding the gains produced by other factors.
At the same time, the research highlights that efficiency improvements are still important for models outside the frontier. Shared algorithmic progress across the industry improved effective compute efficiency by roughly 7.5 times, allowing developers to achieve similar benchmark performance with far less training compute than earlier models required.
In some cases, differences between developers were even more pronounced. The study found that among smaller models, certain developers were up to 61 times more compute-efficient than others when reaching similar performance levels.
Taken together, the findings suggest that the global race to build the most advanced AI systems may ultimately depend less on hidden technological breakthroughs and more on access to large-scale computing infrastructure.
If that trend continues, the researchers note, the availability of advanced chips and data-center capacity could become the decisive factor shaping the future of artificial intelligence.
