Foundation model
Also called base model, frontier model, general-purpose model
A large, general-purpose AI model trained on broad data that many different apps and tasks are built on top of.
A foundation model is a large model trained on broad data at scale, which can be adapted to a wide range of tasks. The term was popularised by Stanford researchers in 2021 to describe models like GPT, BERT and their successors: rather than training a separate model for each task, one general model becomes the foundation, and applications build on it through prompting, fine-tuning or tool integration.
Most AI products today sit on a handful of foundation models from a few large labs, plus a growing set of open-weights models. A writing app, a coding assistant and a customer service bot might all use the same underlying model with different system prompts, tools and interfaces.
The phrase is often used alongside "frontier model", meaning the most capable models at any given time. Regulators also use these terms, since a problem in one widely used foundation model can affect thousands of downstream products. For builders, the choice of foundation model shapes cost, speed, capability, data handling terms and how easily you can switch later.
Example
A legal tech startup does not train its own model. It uses a commercial foundation model through an API, adds a system prompt about legal drafting, retrieval over its clients' documents, and a few fine-tuned classifiers for clause types.
Common mistake
Tying a product too tightly to one foundation model's quirks. Models are updated and replaced often. Keep prompts, evaluations and integration code organised so you can test a new model against your own examples and switch if it is better or cheaper.