Why AI's Future Needs More Flexible GPU Capacity

Modal argues that AI's GPU crunch stems from a mismatch: most demand goes to training, but inference is where revenue comes from, and inference workloads are inherently volatile. Because request rates swing with day-night cycles, weekly patterns, viral traffic and growth trends, fixed long-term GPU reservations push real-world cluster utilization below 50%. Pooling demand across many users — provisioning for the peak of the sum rather than the sum of peaks — can raise utilization sharply, and pooling supply across regions can add further flexibility.