Foundation Models: What They Are and How They're Changing AI

When you talk to a chatbot, get a medical diagnosis from an AI, or see a video generated by software, you’re likely interacting with a foundation model, a large AI system trained on massive datasets to perform a wide range of tasks without needing task-specific training. Also known as large language models, these systems form the base layer of nearly every advanced AI tool you use today. Unlike older AI that was built for one job—like recognizing faces or translating text—foundation models learn patterns from billions of examples and can adapt to many uses. Think of them as general-purpose brains for machines, not specialists.

These models don’t just understand language. They’re now being used to predict protein shapes in biology, spot fraud in banking, and even help design new materials. The same model that writes your email summary might also be analyzing cancer scans or optimizing solar panel layouts. That’s because foundation models are trained on diverse data—text, code, images, and more—making them flexible. They’re not magic, but they’re powerful: a single model can replace dozens of smaller, custom-built AI systems. And they’re driving real change in India’s tech and research scene, from startups building local language tools to labs using them to accelerate drug discovery.

What makes them different from regular AI? Size and scope. They’re trained on enormous datasets, often using billions of parameters. That’s why they need massive computing power—and why costs, energy use, and data quality are big concerns. But they’re also becoming more accessible. Open-source versions are letting researchers in smaller institutions experiment without needing Google or Microsoft’s resources. You don’t need to build one from scratch to use one. Many tools today, like Google’s Gemini or open models from Meta and Mistral, are built on foundation models. The real innovation isn’t just in the model itself, but in how people are adapting it to solve local problems—like improving rural healthcare access or translating regional languages for education.

What you’ll find below are real examples of how foundation models are being applied across science and industry in India—from AI in banking to breakthroughs in medicine and climate science. These aren’t theoretical ideas. They’re working tools changing how research gets done, how decisions are made, and how innovation spreads.