Invest Like the Best with Patrick O'Shaughnessy cover image

Gavin Baker - AI, Semiconductors, and the Robotic Frontier - [Invest Like the Best, EP.385]

Invest Like the Best with Patrick O'Shaughnessy

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The High Marginal Costs and Importance of MFU in AI

AI development faces high marginal costs unlike traditional software that benefits from zero marginal costs. The scaling laws imply that significant investments are required to achieve quality improvements in AI models. While marginal costs can decrease substantially over time due to various factors, they initially remain high at the forefront of model development. Consequently, infrastructure efficiency, highlighted by Model Flops Utilization (MFU), emerges as a crucial success factor for AI companies. Current MFU averages around 35-40%, indicating a notable gap between theoretical compute capacity and actual usage. Higher MFU allows companies to capitalize on efficiencies, enabling faster time-to-market, improved model quality, or reduced operational costs. For instance, even a modest increase in MFU can translate to significant cost savings in inference through techniques like quantization. The competitive landscape potentially renders AI models as commodities, yet as scaling laws progress, the associated costs could prohibitively elevate, transforming scale into a key competitive barrier. Therefore, MFU serves as a vital metric for evaluating AI research success and provides a basis for differentiation among leading companies in this rapidly evolving field.

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