Foundation Models

They turned machine learning from a bespoke engineering project into something closer to a utility. That shift is what makes almost every other AI category on this page possible.

Models are pre-trained on very large corpora, then refined with supervised and preference-based tuning. Adaptation happens through prompting, retrieval or lightweight fine-tuning.

Capability gains are now coming as much from post-training, tool use and inference-time compute as from raw parameter count. Open-weight models trail the frontier but close the gap for many tasks.