CONCEPT
AI Scaling Laws
The empirical power-law relationships — Kaplan (2020), Chinchilla (2022), and subsequent refinements — between model size, training data volume, and computational budget that now function as the AI industry's version of Moore's Law: trend lines acquiring the force of self-fulfilling prophecy.
The AI scaling laws are, in their structural character, exactly what Moore described in 1965: observations fitted to data, stated plainly, and acquiring economic force as an entire industry organizes itself around them. The Kaplan scaling laws, published by OpenAI researchers in 2020, established that language-model performance improves predictably with scale across model parameters, training data, and compute. The Chinchilla laws from DeepMind refined the relationship in 2022, showing that optimal performance requires scaling parameters and training data together rather than one at a time. The empirical observation that training compute required for a given capability has been halving every eight months — sometimes called 'Moore's Law squared' — drives hundreds of billions of dollars in infrastructure investment on the assumption that the next doubling will arrive on schedule.
In The You On AI Field Guide
Moore's framework illuminates what the scaling laws are and are not. Like Moore's original