Increased Compute Efficiency and the Diffusion of AI Capabilities

Understand

Training advanced AI models requires large investments in computational resources, or compute.

• Yet, as hardware innovation reduces the price of compute and algorithmic advances make its use more efficient, the cost of training an AI model to a given performance falls over time - a concept we describe as increasing compute efficiency.
• We find that while an access effect increases the number of actors who can train models to a given performance over time, a performance effect simultaneously increases the performance available to each actor.
• This potentially enables large compute investors to pioneer new capabilities, maintaining a performance advantage even as capabilities diffuse.