Empirical power-law relationships between model size, data, compute, and loss that let labs predict the return on the next training run before they pay for it.
Tensions
Pure training-compute scaling (Chinchilla) is now a special case of a broader cost-equalization problem that includes RL and inference, and the optimum shifts dramatically depending on which terms you include. Public scaling laws also lag what frontier labs actually use, so any number derived from them is a lower bound on real-world precision.
Related Concepts
Chinchilla scaling | cost equalization | inference economics