The ability to distill models from larger ones will be valuable, as companies will likely have AIs tailored for every function they need.

OpenAI has enabled internal distillation processes, allowing for the creation of cheaper models based on their larger, more complex models, enhancing accessibility for developers.

Using larger AI models to teach smaller models allows for the creation of specialized models that fit specific operating domains or cost requirements.

To build efficient models, we need to distill knowledge from larger teacher models, which allows us to tackle significant problems in science and medicine today without waiting for artificial general intelligence (AGI).

Hassabis argues that while smaller, specialized models can be effective, the development of large, general models is essential for achieving significant advancements in AI capabilities. He also mentions that larger models are essential for training the smaller, more efficient models that we use extensively.

The future landscape of AI may involve a combination of large models and smaller, specialized models that work in parallel, leveraging different areas of expertise.

Startups are increasingly opting for smaller AI models instead of the largest models available, as evidenced by recent statistics.

Once a startup has established a working model, they can explore distillation techniques to create smaller, faster models that maintain performance levels.

In the future, we will see a proliferation of different AI models, with companies using the largest models for performance optimization.