Scaling Laws Explained: Why Bigger Models Keep Winning (For Now)
Scaling laws are the empirical rule that predicts how a language model's performance improves as you add compute, parameters, and training data, and they've been the single best predictor of AI progress since 2020. DeepMind's Chinchilla paper corrected the original formula in 2022, showing labs had been building models too large for the data they fed them. As of mid-2026, the live debate isn't whether the law holds — it's whether returns are bending at the high end and whether training data and inference cost, not GPU count, are now the binding constraint. For PMs, that shift changes whether the smarter bet is renting a frontier model, fine-tuning a smaller one, or architecting around inference cost rather than waiting for the next parameter jump.
