AI Model Training

AI model training is the computational process of teaching neural networks to perform tasks by iteratively adjusting their parameters (weights) based on data. For modern large language models, training is a multi-stage pipeline that consumes extraordinary resources and has become one of the defining engineering challenges of the era.

The training pipeline for frontier models typically has three stages. Pre-training exposes the model to trillions of tokens of text (and increasingly images, audio, and video), teaching it to predict the next token. This is the most compute-intensive phase—frontier models require thousands of GPUs running for months, connected by high-speed networks, consuming megawatts of power. Fine-tuning adapts the pre-trained model for specific tasks or behaviors using smaller, curated datasets. Alignment (via RLHF, DPO, or Constitutional AI) shapes the model's outputs to be helpful, harmless, and honest.

The economics of training define the AI industry's structure. Pre-training a frontier model costs $100 million to $1 billion+ in compute alone. This creates a natural oligopoly of organizations that can afford frontier training: Anthropic, OpenAI, Google, Meta, and a handful of others. But the cost of fine-tuning and reinforcement fine-tuning is orders of magnitude lower, enabling a long tail of specialized models built on open-weight foundations.

Training is also where the megascale datacenter challenge originates. The exponential growth in training compute—roughly 4x per year for frontier models—drives demand for HBM, custom silicon, advanced cooling, and increasingly, dedicated power generation including nuclear. Training is the furnace that forges AI capability, and its resource requirements are reshaping energy infrastructure worldwide.