Compute Scaling(Compute Scaling)

Compute scaling refers to improving AI model performance by increasing the computational resources (FLOPs, GPU count, power) allocated to training and inference. Grounded in the Scaling Laws (Kaplan et al., 2020), which showed that simultaneous growth in parameters, data, and compute yields predictable performance gains, it has been the dominant strategy at frontier AI labs since 2020.

In 2026, the emphasis has shifted to include inference-time (test-time) compute scaling alongside training-time scaling, with models dynamically allocating reasoning budget per query. Power procurement and datacenter permitting timelines now set practical ceilings on scaling progress.

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