Foundation Models

Foundation models are large-scale AI models trained on broad, diverse datasets that serve as the base layer for a wide range of downstream applications. The term, coined by Stanford's Center for Research on Foundation Models, captures a key insight: a single powerful model can be adapted to countless specific tasks, rather than building separate models for each application.

The frontier foundation models of 2026—Anthropic's Claude, OpenAI's GPT series, Google's Gemini, Meta's Llama, and DeepSeek—are large language models at their core, but increasingly multimodal: they process and generate text, images, audio, video, and code. They serve as the reasoning engine for AI agents, the generation engine for creative AI tools, and the intelligence layer for enterprise applications.

The economics of foundation models have undergone radical deflation. Training costs remain enormous—hundreds of millions of dollars for frontier models—but inference costs have plummeted 92% in three years. This creates an unusual market dynamic: a small number of labs can afford to build frontier models, but the cost of using them is approaching commodity pricing, especially as open-source alternatives like DeepSeek and Llama compete on quality.

Foundation models are "foundational" in the same way that operating systems are: they create a platform upon which an ecosystem of applications, tools, and agents is built. The Model Context Protocol, agent frameworks, and the agentic web are all application layers that depend on foundation models as their substrate. The quality, cost, and accessibility of these models directly determines the pace of innovation across the entire stack.