AI evals are becoming the new compute bottleneck

As AI models grow larger and more capable, the computational cost and time required to evaluate them has become a significant limiting factor in development, potentially surpassing training compute as the primary bottleneck. Evaluation at scale requires running models across diverse benchmarks and use cases to ensure safety and performance, consuming substantial resources that can constrain how quickly new models can be iterated and deployed. This shift highlights the increasing importance of developing efficient evaluation methods and infrastructure to keep pace with model scaling.

Source: Hugging Face | 2026-04-29