Language Models and Their Evolution
A foundation model is a large-scale AI model developed by a few organizations and made available for others to use as a service.
Learning from Unlabeled Text
A language model’s growth depends on how it can learn from language. The key technique identified here is self-supervision: training targets can be drawn from the text itself, rather than requiring a person to label every example. In this learning setup, a word in the text can serve as the answer to the words that came before it. This gives the model a training signal from raw text and helps explain how language models could grow beyond the scale of earlier systems.
From Language Models to Foundations
The history is a progression, not a sudden replacement. Language models first emerged in the 1950s. As models grew to a very large scale, they became large language models. When a large-scale model is made available for others to build on, it serves as a foundation model. The terms describe related stages: the kind of model, its scale, and how others can use it.
This lineage puts recent applications in perspective. Products such as ChatGPT and GitHub’s Copilot may seem to have appeared quickly, but they build on decades of progress. The source also names Google’s Gemini and Midjourney as examples of widely used products associated with large AI models. The important distinction is that a foundation model is not simply a finished, single-purpose product: it is a base that can support many different applications.
Why Training Becomes Concentrated
As models scale up, training requires data, computing resources, and specialized talent. The source describes those demands as affordable for only a few organizations. This concentrates the work of creating large models, even as those models become more capable and can serve a wider range of tasks. Scale therefore has two effects at once: it can open up more applications, while making the training effort harder for most organizations to take on themselves.
The lower barrier is specifically about getting started without paying the up-front cost of training a large model yourself. It does not mean that every part of building an AI application is cost-free or automatic. The work shifts toward assembling, adapting, and deploying an application around a model that is already available.
A Shared Model as a Service
A foundation model is a large-scale AI model developed by a few organizations and made available for others to use as a service.
The word foundation describes the model’s role: it is a base for many applications rather than a product built for only one purpose. In the model-as-a-service pattern, organizations with the resources to train large models make them available so that application builders can use them without first creating a model of their own. This makes it possible for separate products to build on a shared model.
Choosing How to Build an AI Application
A small product team wants to add an AI-powered feature. Should it begin by training a large model of its own, or build around a model already offered as a service?
- 1Identify the expensive work
Training a large model requires data, computing resources, and specialized talent, and only a few organizations can afford that effort.
- 2Identify the available starting point
A foundation model is made available for others to use, so the team can build without first training its own large model.
- 3Focus the application work
The team’s work can center on assembling, adapting, and deploying an application around the available model.
For this scenario, building around an available model avoids taking on the costly training step. It lowers the barrier to building the application, while leaving application design and deployment work to the team.
Mistakes About Scale and Access
Common mistake Treating foundation models as single-purpose finished products
What it looks likeAssuming a foundation model is built for just one application.
Why it failsThe term describes a large-scale model that others can build on as a base for different applications.
Do this insteadThink of the model as a shared foundation, with applications assembled around it.
Common mistake Assuming every organization can afford to train a model at large scale
What it looks likePlanning to train a large model without accounting for data, computing resources, and specialized talent.
Why it failsThe source says those demands concentrate large-model training in a small number of organizations.
Do this insteadDistinguish training the model from building an application with a model that is already available.
Common mistake Thinking that AI application building began with large language models
What it looks likeTreating recommendations, fraud detection, or churn prediction as practices that appeared only after LLMs.
Why it failsAI applications built on machine learning models existed before large language models became prominent.
Do this insteadRecognize that foundation models change the scale and availability of models while building on earlier ML application practices.
Common mistake Treating the lower barrier to application building as if it removes all application work
What it looks likeExpecting a served model to eliminate the need to assemble, adapt, or deploy an application.
Why it failsThe source describes those activities as part of the work of an AI engineer.
Do this insteadUnderstand the change as avoiding the up-front training effort, not as removing the work of building the application.
Check the Model-Building Trade-Off
A team wants to create an AI-powered application but does not have the resources to train a large model. Explain how a foundation model offered as a service changes the team’s starting point. In your answer, distinguish the cost of training from the remaining work of building the application.
Hints
- Name the resources involved in training a large model.
- Explain what a model-as-a-service provider makes available.
- Include the application work that still needs to be done.
Key Takeaways
- Language models first emerged in the 1950s; large language models represent their growth to very large scale, and foundation models are large-scale models made available for others to build on.
- Self-supervision lets text provide training targets, helping explain how language models could grow to much larger scales.
- Training large models requires data, computing resources, and specialized talent, concentrating that work in a small number of organizations.
- Making foundation models available as a service lowers the up-front barrier for application builders, but they still assemble, adapt, and deploy their applications.
- AI engineering builds on machine-learning application practices that existed before large language models became prominent.