Foundation models are large, general-purpose neural network architectures that serve as the building blocks for artificial intelligence systems. Trained on massive amounts of diverse datasets, these AI models can perform a wide range of tasks involving natural language processing, computer vision and generative AI.
Foundation Model Definition
Foundation models are AI models that have been pre-trained on large quantities of data, which can be fine-tuned for specific tasks across various applications. Specific examples include OpenAI’s GPT and DALL-E models, Meta AI’s Llama models and Stability AI’s Stable Diffusion model.
The versatility of foundation models makes them a key starting point in AI development, as they can be fine-tuned for a variety of specialized tasks. Instead of having to build domain-specific models from the ground up, developers can create whatever AI products they want on top of existing foundation models, making the process faster and more cost-effective.
As a result, foundation models have become the backbone of recent AI innovation, powering many of the tools and technologies we interact with every day.
What Are Foundation Models?
Foundation models are AI models that function as the base — or “foundation” — of many AI products. The term was introduced in a 2021 paper published by researchers at Stanford University’s Center for Research on Foundation Models, who defined it as “any model that is trained on broad data” and that can be adapted to a “wide range of downstream tasks.” And because foundation models can continue to learn from new data, users can tweak prompts to generate comprehensive outputs.
The large size and flexible nature of foundation models make them different from more traditional machine learning models, which are usually designed exclusively for specific, pre-defined tasks. While foundation models are most often linked to large language models (LLMs), which handle text-based tasks, many are becoming multimodal, meaning they can process text, images, video and audio.
Building a foundation model from scratch is expensive and requires an enormous amount of computational power and resources, often taking months to build and train. But once one has been built, businesses can input additional data and fine-tune it for specific tasks with minimal effort, making it a quick and cost-effective tool for making their own AI products.
Why Are Foundation Models Important?
Foundation models provide a versatile and powerful base for developing a wide array of AI products. Their significance stems from their malleability and scalability, which allow businesses to tap into the powers of artificial intelligence without having to start from scratch. This not only saves time and resources, but it also democratizes AI development, empowering more players to leverage the technology in innovative ways.
As foundation models continue to grow more sophisticated, they will inevitably influence the quality of the AI products built on top of them — and, by extension, the industries using them. In many ways, the future is being shaped by advancements in foundation models.
How Do Foundation Models Work?
Foundation models leverage deep learning techniques (specifically neural networks) to process and interpret terabytes of data, learning its underlying structure and nuances. This data is extensive — often encompassing text, images, video and audio — which gives the model a broad understanding of various subjects that can be applied in different ways.
The versatile nature of foundation models is made possible through self-supervised learning and transfer learning, where the model learns patterns and relationships from unlabeled data and then applies that information from one situation to another. It’s similar to how humans learn: If a person learns to drive one car, for example, they can drive most other cars fairly easily — and may even be able to figure out how to operate a bus or a boat, too.
After they’ve been trained, foundation models can be modified with additional data to specialize in specific tasks.
“It’s easier to teach a child who knows how to speak already than to teach a baby how to speak,” Susan Nomecos, senior director of global AI and Web3 strategy at Getty Images, told Built In. “It’s kind of the same thing with these foundation models. They’re trained to understand a lot of different contexts, and be able to perform a lot of different tasks already.”
For instance, a model trained on lots of diverse text data will understand the relationship between words, how to properly structure a sentence and a range of writing styles. If that model is then fine-tuned on healthcare data, it could be used to power a chatbot that helps doctors diagnose patients, fluently answering health-related questions and providing relevant advice.
“Fine-tuning is essentially the ability to personalize a version of a foundation model,” Brennan Woodruff, co-founder and chief business officer at generative AI company GoCharlie, told Built In. “You’re basically giving it more data so that it is more accurate to you and what you need to do.”
Applications of Foundation Models
While foundation models have seemingly infinite applications, these are some of the most common ways they’re used.
Natural Language Processing
Natural language processing enables AI models to interpret written and spoken language, bridging the gap between human communication and computer understanding. These models have the ability to converse with users in a human-like way, summarize text inputs, generate their own content and translate languages.
Computer Vision
Computer vision focuses on processing and understanding visual data, allowing AI models to identify and analyze objects, actions and other relevant features. These models are pre-trained on data sets of annotated images and videos, and can be fine-tuned for more specific tasks like facial recognition and machine vision.
Generative AI
Some foundation models are built to create completely new content, which involves producing text, images, audio and video that mimic human-made works. These models are good at finding patterns in large quantities of data and then replicating those patterns in new creations. And they can be fine-tuned with additional data to generate content that’s more tailored to the user’s specific needs.
Coding
Foundation models also have the ability to work with code-specific problems. Besides producing code in a number of programming languages, foundation models can help evaluate code and detect bugs and other errors. Users less experienced in coding can even ask models to provide explanations of the code they generate.
Research and Discovery
Scientists and academic researchers can use foundation models to speed up the research and discovery process. With their ability to handle massive volumes of data, foundation models can summarize numerous research papers and identify common trends. This saves researchers time and can facilitate new findings and breakthroughs.
Use Cases of Foundation Models
Industries are transforming in the wake of foundation models, including sectors like healthcare, marketing and education.
- Law: Foundation models can be fine-tuned to help law professionals research relevant case law, statutes and legal opinions by efficiently sifting through thousands of documents, summarizing their contents and highlighting key passages.
- Healthcare: Trained on images from x-rays, MRIs and CT scans, foundation models can learn to identify abnormalities like tumors and fractures in healthcare settings.
- Marketing: Foundation models can be used to generate blogs, social media posts, emails and other marketing materials tailored to specific audiences.
- Automotive: Foundation models can generate life-like driving scenarios, which can be used as simulations to train autonomous vehicle systems.
- Education: In the face of teacher shortages, foundation models can develop personalized lesson plans for individual students and offer AI tutoring services. In addition, they can support teachers with feedback and aid with lesson planning.
- Logistics: Autonomous mobile robots are powered by foundation models, which give these robots the ability to quickly learn new tasks and adapt to unfamiliar situations. This allows them to safely navigate changing environments like warehouses and factories.
Examples of Foundation Models
Some of the more prominent examples of foundation models available today include GPT, Claude 3, Llama, DALL-E and Stable Diffusion.
GPT
The Generative Pre-trained Transformer (GPT) model was first introduced by OpenAI in 2018. Since then, the company released several improved versions of the large language model. The latest version powers OpenAI’s popular ChatGPT chatbot and can be accessed via the company’s API.
Claude 3
Claude 3 is a family of large language models developed by Anthropic. Companies can now fine-tune Claude 3 Haiku — the fastest and most affordable of the models — to adapt its knowledge and abilities to their specific business needs, making it more effective for specialized tasks.
Llama
Llama is a family of large language models developed by Meta AI to perform natural language processing tasks. These foundation models come in various parameter sizes, with larger ones designed for more complex tasks and smaller ones offering greater efficiency. The latest version, Llama 3.1, is open source and available for download on Hugging Face and Meta’s website.
Mistral
The Mistral foundation models are a family of large language models developed by Mistral AI. Some of the Mistral models are open source, while others are accessible exclusively through its API. In 2024, the company released Pixtral, which is capable of processing images and documents in addition to text.
DALL-E
DALL-E is a model that generates images from text inputs, transforming written prompts into visual art. OpenAI released the first iteration of the model in 2021 and has since come out with several more advanced versions, the latest of which is called DALL-E 3. The model is built natively in ChatGPT and is available via OpenAI’s API.
Stable Diffusion
Stable Diffusion is a model that can create images from text prompts, enabling users to generate highly detailed visuals with just a few written words. Developed by Stability AI, this model employs a process called diffusion to make its images, meaning it iteratively refines random noise into coherent visuals, capturing intricate details and styles. Stable Diffusion is one of many foundation models available on Amazon Bedrock.
DeepSeek-R1
Introduced in January 2025, DeepSeek-R1 sent shockwaves through the AI market. The model is much smaller than typical foundation models, yet it matches the performance of those created by juggernauts like OpenAI, Google and Meta. DeepSeek-R1 is mostly trained through reinforcement learning and is open source, giving larger audiences access to potentially game-changing AI technology.
Nvidia Cosmos
Nvidia Cosmos is a platform that supports a family of foundation models dedicated to “physical AI development.” In this case, physical AI refers to AI being integrated into machines like autonomous vehicles and robots. Having been trained on 20 million hours of video data, these models can be used for visual tasks like conducting video searches, generating synthetic visual data and building multiverse simulations.
Amazon Nova
Amazon Nova is Amazon’s family of multimodal foundation models that work with text, image and video inputs and outputs. With these models, users can perform actions like generating code, creating video reels and translating text into different languages. The Nova family is designed to be cost-effective and is available only on Amazon Bedrock.
Foundation Models vs. Traditional Models
To fully understand the promise of foundation models, it helps to compare them to their predecessors. Here’s how foundation models stack up against traditional models in a few areas, from the training process to real-world applications.
Training Process
Traditional models are typically trained on logic instead of raw data. Essentially, programmers teach models to follow a specific set of rules. On the other hand, foundation models are trained on vast amounts of raw data. They are typically powered by a transformer architecture, allowing them to identify general patterns within this unlabeled data and apply their knowledge to new data.
Resource Requirements
Traditional models are trained on smaller data sets and use logic to complete specific tasks. Meanwhile, foundation models require much larger amounts of data to be able to generalize patterns they learn from the training data. They also run on transformer networks, adding to their computational needs.
Capabilities
Traditional models are trained to complete a particular task — they can’t break from the logic instilled in them. Foundation models are taught to learn more general patterns that they can apply to different situations. As a result, foundation models can learn how to perform new tasks they were never trained to do.
Applications
Foundation models can quickly pick up new tasks and skills with simple instructions, unlike traditional models. They can then take on more complex tasks like generating videos based on text prompts, fully automating nuanced conversations with customers and assessing code for errors. Traditional models demonstrate much more limited capabilities, like responding to basic questions from customers with pre-trained answers or generating a text response to address a text-based inquiry.
What Are the Challenges of Foundation Models?
Foundation models serve as a solid starting point in AI development, but they are not without flaws. As a single point of failure, any errors, vulnerabilities and biases within a model can spread to all of the AI products built on top of it, amplifying the risks.
Lack of Interpretability
The inner workings and decision-making processes of foundation models are often not well understood — even by the people actually making them — which makes it hard to determine how and why they arrive at certain conclusions.
“These are little black boxes,” Rajul Rana, chief technology officer at IT services company Orion Innovation, told Built In. “We know roughly how they work but [we] don’t know exactly why they generate certain outputs.”
This lack of interpretability can make it difficult to trust foundation models’ outputs or correct any errors, which can have massive consequences — especially since they are embedded in our everyday lives, from the facial recognition software used to unlock phones to the hiring algorithms companies use to screen job candidates.
“You really run the risk of perpetuating a lot of bias and misinformation,” Nomecos said.
Privacy Risks
Because users can input proprietary or otherwise sensitive data during fine-tuning, using foundation models can pose a privacy risk. If not properly protected, this data could be leaked — either through security vulnerabilities in the system or by the model itself. And since foundation models are often shared across multiple users, they could inadvertently reveal private information if it is not carefully managed.
Unreliable Answers
Sometimes foundation models generate outputs that appear correct, but aren’t. They don’t always fully understand the context or meaning behind the information, relying instead on patterns learned during training. So, when they are faced with incomplete, ambiguous or unfamiliar inputs, they may fill in the gaps with incorrect information — a phenomenon known as “hallucination.”
As a result, foundation models “never get to 100 percent accuracy,” Woodruff said. “It’s that degree of unpredictability that makes them hard to work with.”
Environmental Impact
AI has a major climate change problem because it requires large amounts of energy at various stages, from training foundation models to processing user requests. The computational resources needed to sustain AI tools quickly add up.
In addition, the infrastructure behind AI comes with its own set of issues. Data centers help maintain AI technologies, and these centers consist of computers that demand hefty amounts of raw materials. According to a 2024 United Nations report, building a 2-kilogram computer takes 800 kilograms of raw materials. These kinds of environmental costs may outweigh the benefits of even the most energy-efficient AI systems.
Data Scarcity
To be accurate and effective, foundation models must be trained on large and diverse data sets. But high-quality data isn’t always easy to access, especially when considering AI copyright challenges. Failing to feed a foundation model enough data for training can lead to more biases and undermine its ability to learn general patterns and apply them to new data.
Frequently Asked Questions
What is an example of a foundation model?
One prominent example of a foundation model is OpenAI’s GPT-4, which was trained on petabytes of text and image data to perform tasks like text generation, image analysis and even web development. The model can also be fine-tuned for more specialized tasks, like powering a customer service chatbot or assisting with legal document analysis.
What are the characteristics of foundation models?
The main characteristics of foundation models are their large size and versatility. These models are typically trained on massive datasets, allowing them to handle a diverse range of tasks, and they can be fine-tuned for specific applications with minimal extra training.
What is the difference between generative AI and foundation models?
Foundation models are the backbone of generative AI, providing the large-scale architectures needed to create new text, images, video and audio. Because these models are good at learning patterns from vast amounts of data, they can generate outputs that mimic human creations.
What is the difference between deep learning and foundation models?
Deep learning is a core technology in foundation models, which use neural networks to process large quantities of data and identify patterns within them. Foundation models are capable of performing complex tasks like image generation and language understanding because of deep learning.
