Foundation model

Plain English. One large model, pretrained broadly, adapted to many downstream tasks — the foundation others build on. The term was coined at Stanford in 2021 to name the shift from task-specific models to general substrates. It now travels beyond language: labs are minting foundation models for biology, weather, materials and 3D worlds.

Why it moves money. Foundation models carry platform economics. Whoever owns the substrate collects rent from everything built on it, which is why "we are building the foundation model for X" is the highest-multiple pitch in venture — each new domain is a claim that platform economics will replay there. The claim is checkable: a genuine foundation model beats task-specific baselines across its domain, on measured performance, not on vision decks.

What to watch. Whether domain foundation models post measured wins over specialised incumbents — hit rates, forecast skill, discovery counts — and whether adaptation on top of them stays cheap enough to leave room for an application layer.

From the signals. OpenAI launched biology-tuned GPT-Rosalind. World Labs launched Atlas, an omni world model over text, image, video and 3D. Anthropic reported protein-binder hit rates above its stated field norm.

Further reading. Bommasani et al., On the Opportunities and Risks of Foundation Models (2021)