The trend of purpose-trained models that are smaller and customized for specific domains is gaining traction, as they can outperform general models in targeted applications.

The trend in machine learning is towards convergence, with many different types of models shifting to a more unified transformer model.

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.

The future will consist of many smaller, specialized models for different use cases, rather than just one generic model.

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

Startups can be built by combining existing general models with domain-specific knowledge, and it's crucial to push both general and specialized techniques to advance towards AGI.

In the future, foundational models may become highly specialized, such as models specifically designed for chip design or protein folding.

As AI becomes more integrated into various applications, we need to optimize for specific internal use cases, which may lead us to use other models if they better serve those needs.