What are foundation models? In AI, this term refers to a new class of large neural network models trained on broad, diverse data. These models learn general patterns across massive datasets so they can be adapted to many tasks. For example, foundation models are designed to generate a wide variety of outputs – from text to images or audio – and they can serve as a base for other AI applications.
In short, a foundation model is a large, pre-trained model that provides a “foundation” of knowledge. Companies train these models on billions of words or millions of images. Later, developers fine-tune or prompt them for specific tasks (like translation or summarization). This approach contrasts with older “narrow” AI models that were trained for one task only.
Researchers first popularized the term in 2021. Stanford’s Center for Research on Foundation Models (CRFM) defined them as “models trained on broad data (generally using self-supervision at scale) that can be adapted to a wide range of downstream tasks.”. In practice, foundation models underpin many modern AI applications.
For instance, Stanford notes that ChatGPT and Microsoft’s Bing Chat use the GPT family of foundation models. Google’s BERT is another example of an early foundation model for language.
History of Foundation Models
Foundation models emerged from a series of AI breakthroughs in the 2010s and early 2020s. Key milestones include:
- 2017 – Transformers: The transformer architecture (by Vaswani et al.) revolutionized AI by enabling models to learn language context effectively.
- 2018 – BERT: Google released BERT, a large language model trained on massive text. BERT achieved state-of-the-art results in many NLP tasks and was made open-source. This spurred a race to build even bigger models.
- 2020 – GPT-3: OpenAI introduced GPT-3, a generative language model with 175 billion parameters. GPT-3 was trained on nearly a trillion words and could write poems, answer questions, and generate code, showing new emergent capabilities.
- 2021 – Coining the Term: In August 2021, Stanford’s AI researchers coined the phrase “foundation model” to describe this new paradigm. They published the paper “On the Opportunities and Risks of Foundation Models” to analyze these systems.
- 2022 – Multimodal Models: OpenAI released GPT-3.5 and later GPT-4, which could handle text and images. Image generators like DALL·E 2 (OpenAI) and Stable Diffusion (Stability AI) also became popular in 2022. The AI community saw the dawn of true multimodal foundation models (models handling text, images, audio, etc.).
- 2023 – Explosion of Models: According to the Stanford AI Index (2024), 149 new foundation models were released in 2023 – more than double the number from 2022. Notably, 65.7% of these were open-source. This rapid growth shows how fast the field is advancing.
These milestones reflect how AI shifted from task-specific models to large, general-purpose models. As Stanford’s AI Index notes, by 2023 foundation models were the dominant paradigm in AI development.
Key Characteristics of Foundation Models
Foundation models share several defining features:
Scale and Data
They are extremely large. Training often involves billions of words or millions of images collected from the internet. For example, GPT-3 used a dataset of nearly a trillion words. Building such models requires vast compute power and data.
Self-Supervised Pretraining
These models use self-supervised learning. They train on raw unlabeled data by predicting parts of it (e.g. predicting the next word in a sentence or filling in missing words). This lets the model learn language or vision patterns without hand-labeled data.
Transfer Learning
After pretraining, the model is fine-tuned for specific tasks. In other words, they transfer learned patterns to new tasks. For example, one study notes that a foundation model can apply information learned in one context to a different task. This means developers can adapt the base model to specialized needs with relatively little new data.
Versatility
A single foundation model can serve many tasks. By design, it is a generalist. For instance, an LLM like GPT-4 can write essays, answer questions, summarize texts, and even generate code. Some models are multimodal: they can handle multiple input types (text, images, audio) and produce corresponding outputs.
Emergent Abilities
As scale grows, these models often show new capabilities not explicitly trained for. Stanford’s report highlights that simply increasing model scale leads to “emergent capabilities” beyond those of smaller models. Users found GPT-3 could solve problems and reason in ways that went beyond its explicit training tasks.
Homogenization
Many applications now use the same base models. For example, both Bing Chat and Duolingo’s Max tutor use versions of GPT-4. This homogenization is powerful but also risky: any flaw in the base model carries over to all uses.
Resource-Intensiveness
Training foundation models is very expensive. The largest models can cost tens of millions of dollars in compute. The AI Index reported GPT-4’s training compute was about $78 million, and Google’s Gemini Ultra cost far more. However, once trained, using (fine-tuning or inferring with) these models is far cheaper than training from scratch.
Each of these characteristics helps explain why foundation models are a new paradigm. Their large scale and generality make them different from previous AI models.
How Do Foundation Models Work?
Foundation models typically use transformer neural networks with attention mechanisms, though the core idea is training on broad data and then fine-tuning. First, the model is pre-trained on massive datasets. For language models, this means feeding it billions of text examples to predict missing or next words. For vision models, it might involve predicting parts of images or matching captions to images. Through this self-supervised learning, the model learns grammar, facts and patterns without explicit labels.
Next comes adaptation or fine-tuning. Developers feed the pretrained model a smaller labeled dataset for a specific task. Because the model has already learned language or vision at scale, fine-tuning needs far less data. For example, IBM found it could train a sentiment model in a new language using only a few thousand sentences – about 100 times fewer annotations than before. In fine-tuning, the model’s parameters adjust to the task, leveraging its broad knowledge while specializing on the new data.
In practice, many foundation models are also used via prompts and APIs. Instead of further training, users can give the model a prompt (like “Write a summary of…”), and the model generates an answer. Some systems combine the model with tools or retrieval (so-called retrieval-augmented generation) to improve accuracy. The key is that the vast pretrained knowledge can be accessed quickly by developers and end-users, either by fine-tuning or by smart prompting.
Behind the scenes, these models rely on deep learning principles. They employ unsupervised or self-supervised training (learning patterns from data without labels), and then use transfer learning to apply that knowledge. As one source explains, foundation models “capture general knowledge about language and context” from massive corpora, which can then be fine-tuned on specific applications. Because the models are so large, they can model complex relationships and long-range context.
What Are Examples of a Foundational Model?
Many well-known AI models today are foundation models. Examples include:
GPT-4 (OpenAI)
A large language model that can generate human-like text. It powers applications like ChatGPT. GPT-4 was trained on vast text data and can answer questions, write essays, and more.
GPT-3 (OpenAI)
The predecessor to GPT-4, with 175B parameters. It demonstrated many emergent abilities (story writing, coding, etc.) after its 2020 release.
BERT (Google)
A transformer-based model trained on billions of words. BERT introduced bidirectional training and is used for search and question-answering. It was one of the first public models that shifted NLP toward large pretrained models.
DALL·E 2 (OpenAI)
A multimodal model that generates images from text prompts. It is trained on a large dataset of image-text pairs and can create realistic images from descriptions.
Stable Diffusion (Stability AI)
An open-source image generation model. It can produce photo-realistic images given text prompts, and has been widely adopted in creative communities.
Bloom (BigScience/Hugging Face)
A multilingual language model with 176B parameters. Bloom can generate text in many languages (46 natural languages and 13 programming languages), reflecting the trend toward open and collaborative foundation models.
ChatGPT (OpenAI)
Although ChatGPT itself is an application, its core engine (GPT-3.5/4) is a foundation model. ChatGPT provides conversation, answers, and creative writing. In fact, Stanford notes that “the popular application ChatGPT is built on the GPT-3.5 and GPT-4 families of foundation models”. Yes – ChatGPT is a foundation-model-based chatbot.
Claude (Anthropic)
A conversational AI similar to ChatGPT, built on Claude foundation models (Anthropic’s large language models).
PaLM (Google)
A large language model by Google (e.g. PaLM 2) used for tasks like translation and chat (Bard is based on PaLM).
Midjourney (Midjourney Inc.)
Uses a foundation model for image generation (Discord bot that creates art from text).
PaLM-E (Google DeepMind)
A multimodal model combining language and vision for robotics tasks.
Gato (DeepMind)
A “generalist agent” model trained to perform many tasks (play games, caption images, control robots).
These are just a few examples. They include pure text models (GPT, BERT, Bloom), vision-and-text models (DALL·E, CLIP), and multimodal models (PaLM-E, Gato). All of them were trained on broad data and can be fine-tuned for many purposes. Notably, many recent foundation models are open source (like Bloom, Stable Diffusion), which means developers worldwide can use and improve them. This list shows the variety of foundation models available today, spanning language, vision, and multimodal AI.
How Are Foundation Models Different From Large Language Models (LLMs)?
Large Language Models (LLMs) are actually a subset of foundation models. LLMs are foundation models specialized in processing and generating text. For example, GPT-4 and BERT are LLMs – they handle language data. However, not all foundation models are LLMs. By definition, a foundation model can be trained on any modality (text, images, audio, etc.) and serve multiple tasks. An LLM only deals with text (or sometimes text+code).
In practical terms, every LLM is a foundation model, but not vice versa. Iguazio explains it this way: foundation models are large models trained on massive datasets for multiple tasks, whereas LLMs are specifically those foundation models that perform natural language tasks. In other words, LLMs focus on language understanding and generation, but a foundation model might also encompass image generation (like DALL·E) or video understanding (like Vision-Language Models).
Another way to see the difference: The term foundation model highlights the general-purpose nature. A foundation model could underlie a text engine or an image tool. The term LLM highlights the language aspect. For example, GPT-4 is both a foundation model and an LLM. By contrast, DALL·E 2 is a foundation model for images but not an LLM at all.
In summary, LLMs are essentially language-focused foundation models. When comparing foundation models vs large language models, remember: LLMs fall under the broader umbrella of foundation models. The key difference is scope (foundation models cover all kinds of AI tasks; LLMs cover language tasks).
Applications of Foundation Models
Foundation models are already powering many real-world applications across industries. Some notable examples include:
Conversational AI and Chatbots
Many modern chatbots use foundation models. For instance, OpenAI’s ChatGPT and Anthropic’s Claude provide conversational Q&A, customer support, or tutoring functions. Microsoft’s Bing Chat is built on GPT-4. These systems can answer questions, write summaries, and even help draft documents.
Content Creation
Foundation models generate content in text and images. GPT-4 can write articles, poems, and code. Image-generation models like DALL·E 2, Midjourney, and Stable Diffusion allow users to create realistic images from simple text descriptions. Designers use these to prototype visuals, and marketers use them to create ads or art.
Education and Tutoring
AI tutors and study aides are emerging. For example, Khan Academy’s “Khanmigo” uses GPT to help students with questions and lesson plans. Duolingo Max uses a foundation model to generate language learning exercises and roleplay conversations with learners. These tools can adapt to the learner’s level and provide personalized feedback.
Search and Assistance
Google’s search engine now uses models like BERT and PaLM to better understand queries. AI assistants like Google Assistant and Apple’s Siri may soon use them to answer questions in more natural language. Grammarly and other writing tools use LLMs to suggest edits and improvements.
Business Intelligence
Companies apply foundation models to data analysis. For example, they can automate report generation by summarizing business metrics. AI models also assist in coding (e.g. GitHub Copilot uses a GPT-based model to suggest code). In finance and healthcare, models help sift through documents or medical records to extract insights.
Robotics and Simulation
Nvidia and others are integrating foundation models into physical world tasks. Nvidia’s “world model” foundation models simulate realistic 3D environments. These models help train autonomous vehicles and robots by generating virtual data. In factories, “digital twins” (simulated replicas of production lines) use foundation models to find efficiency improvements.
Science and Medicine
Researchers are exploring foundation models for drug discovery, protein folding, and genomics. While not all such models are public, the same techniques that power language models (like transformers) are used in models like DeepMind’s AlphaFold for proteins. Foundation models accelerate literature review by summarizing research papers in law, healthcare, and science.
Creative Arts
Musicians and filmmakers use AI to aid creativity. Models can help generate music (e.g., OpenAI’s MuseNet) or suggest story ideas. Virtual reality and game development will use foundation models to create dynamic narratives or characters.
These applications show how foundation models act as the “engine” behind many AI tools. From chatbots and translators to image editors and industrial simulators, the same core idea – a large pretrained model – supports them all. In some cases, companies fine-tune a model for a domain (like finance or legal), or they use the model as-is via an API. For example, IBM’s Watson team uses GPT-like models to handle multiple languages in enterprise settings. As more models become available, we will likely see foundation-model-based tools in areas we have not even imagined yet.
Benefits of Foundation Models
Foundation models offer many advantages:
Faster Development
By starting with a pretrained model, developers skip the need to collect vast labeled datasets from scratch. IBM reports that training a language model for a new language now takes about 100 times fewer labeled examples than before. This makes it much faster and cheaper to build new AI applications.
High Accuracy
Pretrained models often achieve superior accuracy. Since they learn from massive data, they capture nuances in language and vision. IBM found that their Watson NLP models, built on foundation models, “surpass accuracy achieved by the previous generation of Watson” in tasks like sentiment analysis.
Versatility
One foundation model can be fine-tuned for many tasks. This means companies do not need a different model for each function. For example, a single GPT model can handle translation, Q&A, and writing. This reuse of a powerful base model reduces costs.
Multilingual and Multidomain
Foundation models can cover many languages and domains. IBM’s example: using them, Watson’s NLP jumped from supporting 12 languages to 25 languages in one year. Similarly, one model can be used across healthcare, finance, or legal texts without retraining from scratch for each field.
Open Access & Collaboration
Many foundation models are open-source, which democratizes AI. The Stanford AI Index notes that in 2023, 65.7% of new foundation models were open-source, up from 33.3% in 2021. Open models (like Bloom or Stable Diffusion) allow researchers and startups to build on them freely. This accelerates innovation across the industry.
Innovation Acceleration
Stanford’s AI Index also highlights that foundation models have led to an “explosion” of new capabilities. Researchers find that these models can often perform tasks in a “zero-shot” or “few-shot” way (with no or minimal additional training). For instance, GPT-3 could often complete tasks just from a text description. This means teams can prototype AI features very rapidly.
Consistency Across Products
Companies benefit from using the same foundation model in multiple products. For example, OpenAI uses GPT for ChatGPT, Copilot (coding assistant), and other APIs. This consistency simplifies maintenance and ensures improvements in the base model help all products.
In short, they significantly reduce the effort to build AI systems. They leverage scale and transfer learning to deliver high performance out of the box. As IBM notes, they “dramatically accelerate AI adoption in enterprise” by cutting down labeling needs and enabling broad automation.
Challenges And Risks Of Foundation Models
Despite their promise, foundation models bring important challenges and risks:
Bias and Fairness
Because these models are trained on internet data, they can reflect and amplify societal biases. NVIDIA warns they can “amplify bias implicit in the massive datasets” used for training. For example, a model might generate biased language or make unfair assumptions in its outputs. This raises concerns about equity and fairness in applications like hiring or lending.
Misinformation and Hallucinations
Foundation models sometimes produce false or misleading information confidently. GPT-3 and others have been shown to “hallucinate” facts. This is dangerous if users trust the output blindly. NVIDIA points out the risk of “introducing inaccurate or misleading information” in generated content. Real-world mistakes could range from bad customer advice to false scientific claims.
Intellectual Property
Training on vast copyrighted data can violate IP laws. The models learn from books, art, and code scraped from the web. NVIDIA notes they may inadvertently “violate intellectual property rights of existing works”. This has led to legal debates over whether AI outputs infringe on the original creators of training data.
Privacy
These models may memorize and expose sensitive information if not carefully managed. For example, personal data included in training sets (like email addresses or medical details) could be regurgitated. This risk requires attention to how training data is collected and filtered.
Environmental Impact
Training them consumes huge amounts of energy. IBM pointed out that training one large NLP model can have a carbon footprint equivalent to running five cars over their lifetimes. As models grow, the required computing and energy use become significant. This raises sustainability concerns.
Homogenization Risk
A single foundation model can power many systems. While this is efficient, it also means any flaw or vulnerability is inherited everywhere. Stanford emphasizes that model “defects of the foundation model are inherited by all adapted models downstream”. A bias or bug in the base model could propagate through countless applications.
Security and Misuse
These powerful models can be misused for harmful purposes. For example, they can help generate believable phishing emails or deepfake text. There is also a risk of adversarial attacks (carefully crafted inputs that trick the model). Ensuring robust security is an ongoing challenge.
Regulatory and Ethical Issues
Foundation models operate at a scale that outpaces existing regulation. They raise new policy questions around AI governance. For instance, the EU AI Act now explicitly defines general-purpose AI models (foundation models) under higher-risk categories. Policymakers are concerned about transparency, accountability, and bias. As one industry group said, “future AI systems will likely rely heavily on foundation models, [so it] is imperative… to develop more rigorous principles” for safe deployment.
Experts are actively researching solutions. Ideas include filtering training data, adjusting model outputs on the fly, or requiring disclosures of AI-generated content. For now, it is clear that they must be used responsibly.
Future of Foundation Models
Looking ahead, foundation models will continue to transform AI and spark new trends:
Agentic AI
McKinsey highlights the rise of agentic AI, where foundation models are given more autonomy to act. An “agentic” system uses a foundation model as a “virtual coworker” that can plan and execute multi-step tasks, rather than just respond passively. Think AI assistants that can independently handle booking meetings, researching topics, or even coding, by leveraging a foundation model’s knowledge. This could make AI more proactive and useful in workflows.
Multimodal and Robotic Models
Researchers are pushing foundation models into the physical world. Models like Google DeepMind’s PaLM-E combine vision and language to instruct robots. Nvidia and others are building “world models” that learn from video and simulation. These models (trained on driving videos, for example) could let robots and cars understand environments better. We may soon see them control robots via both text and vision, bridging AI with robotics.
AI in the Metaverse
Foundation models will power virtual worlds. Nvidia notes they will simplify development of the metaverse (3D virtual spaces). For example, they can generate realistic avatars, NPC dialogue, or even entire virtual environments on the fly. As the line between digital and physical blurs, they could animate games, VR training simulations, or digital art tools in the metaverse.
Specialized Hardware
The demand for compute is pushing new chip innovations. McKinsey reports that AI has become the primary driver for new semiconductor development. Expect more advanced GPUs, TPUs, and custom AI chips optimized for large models. Energy efficiency will improve through techniques like mixture-of-experts architectures, which activate only parts of the model as needed. These hardware advances will make it cheaper and faster to train and run foundation models.
Accessibility and Democratization
The trend toward open-source may continue. We expect more publicly released models (like LLaMA by Meta, which was released earlier) and community-driven improvements. Lowering barriers will let small companies and researchers build on cutting-edge models. We may also see cloud services offering easy fine-tuning of foundation models for specialized tasks (some of which already exist, like Azure’s OpenAI Service or Hugging Face’s tools).
Regulation and Standards
As foundation models become ubiquitous, governments and industry groups will develop standards. The EU, US, and others are already discussing regulations for general-purpose AI. Organizations like IEEE and ISO may create guidelines on transparency, bias testing, and safety for foundation models.
Toward General AI?
Some researchers think that as foundation models scale and improve, they could take us closer to more general artificial intelligence. GPT-4 already showed reasoning skills that felt like steps toward human-like understanding. If this continues, in the future they might handle more complex, abstract tasks with less guidance. However, this is speculative and requires solving major technical and ethical issues.
Conclusion
Overall, the future of foundation models is intertwined with the future of AI. They are likely to become even bigger, smarter, and more widely used. At the same time, the AI community is focusing on making them safer and more efficient. As one report puts it, we are “in a time where new capabilities are exploding” due to these models. The path ahead will involve both innovation in capabilities (what these models can do) and caution about their impact. Regardless, they will remain central to AI’s future.
At Designveloper, we’ve witnessed firsthand how foundation models are reshaping the way businesses build and scale digital products. As a leading web and software development company based in Vietnam, we don’t just observe these trends—we help our clients harness them.
Whether you need a custom AI solution, web application, mobile app, or enterprise software with intelligent features, we’re equipped to guide and build with you. Our engineers and AI researchers continually explore how models like GPT-4, BERT, or open-source LLaMA variants can be tailored to solve real-world problems.
