Definition
A foundation model, also referred to as general-purpose AI (GPAI) is a type of AI capable of a range of general tasks (such as text synthesis, image manipulation and audio generation). Examples include OpenAI’s GPT-3 and GPT-4, foundation models that support the conversational chat agent ChatGPT [1].
In contrast to conventional AI models that are often designed and trained for specific tasks, foundation models are known for their ability to learn a wide range of tasks and skills during their initial training phase, more versatile and capable of being adapted and fine-tuned for various applications. Their flexibility is what makes them foundational, serving as a basis upon which various specialized models and applications can be built.
A foundation model is like secondary school education where students learn mathematics, English, sciences, social studies, arts—a comprehensive general knowledge foundation covering many subjects. After completing WAEC with this foundational education, students then specialize: some enter university to study Medicine (fine-tuned for healthcare), others pursue Engineering (fine-tuned for construction and technology), some study Law (fine-tuned for legal work), and others enter Mass Communication (fine-tuned for media). The same secondary school foundation enables all these different career paths. Similarly, foundation models receive extensive general training on massive datasets, learning broad language patterns, reasoning, and knowledge, then get fine-tuned for specific applications—translating languages, writing code, analyzing financial data, or generating creative content.
Origin
The term
"foundation model" was popularized in a 2021 paper by the Stanford
Center for Research on Foundation Models and the Stanford Institute for
Human-Centered Artificial Intelligence (HAI).
Its origin can
be traced back to the development of large-scale machine-learning models. In contrast
to traditional AI models trained for specific tasks, foundation models are
trained on expansive datasets, enabling them to be fine-tuned for a myriad of
applications and downstream tasks.
Examples of foundation
models include GPT-4, Dall-E 2, and BERT [3].
Context and
Usage
- Code generation: Foundation models can generate, evaluate and debug various programming language codes using natural language inputs.
- Human-centered engagement: They support human decision-making such as in clinical diagnoses, decision support systems, and analytics.
- Language processing: These models have remarkable capabilities to answer natural language questions and even the ability to write short scripts or articles in response to prompts. They can also translate languages using NLP technologies.
- Speech to text: Tasks that involve speech to text such as transcription and video captioning in a variety of languages depend on foundational models
- Visual comprehension: FMs excel in computer vision, especially with regard to identifying images and physical objects. These capabilities may find use in applications such as autonomous driving and robotics. Another capability is the generation of images from input text, as well as photo and video editing [4].
Why it Matters
Foundation
models have become central to AI, driving a new era of versatile and adaptable
machine learning. Foundation models are ground works for building specialized
solutions, rapidly speeding up development. These formidable architectures,
pre-trained on massive datasets, have powered major advancements in natural
language processing, computer vision, and beyond.
With their
sophisticated capabilities in understanding language, images, and multimodal
data, foundation models have become cornerstones ushering in transformative AI
applications. Vision models like ResNet enable remarkable image analysis, while
language models such as BERT and GPT excel in generating human-like text [5].
Related AI
Models and Architectures
- Generative Pre-trained Transformer (GPT): Family of language models using transformer architecture trained on vast text data.
- Hidden Layer: Intermediate layer in a neural network between input and output that processes data.
- Large Language Model: AI model trained on massive text datasets to understand and generate human language.
- Latent Space: Abstract mathematical space where AI models represent data in compressed, meaningful dimensions.
- Mixture of Experts: Architecture that uses multiple specialized sub-models coordinated by a gating network.
In Practice
A real-life case
study of foundational models in practice can be seen in the case of Anthropic.
They specialize on AI safety and have developed the Claude family of models. The
Claude 3 series (including Opus, Sonnet, and Haiku) is known for its large
context windows and strong reasoning capabilities.
References
- Jones, E. (2023). What is a foundation model?
- Merritt, R. (2025). What Are Foundation Models?
- Shah, D. (2025). Foundation Models Explained: Everything You Need to Know.
- AWS. (2026). What are Foundation Models?
- Yun, C. (2024). The Foundation Model: Key Facts and Insights.