While specialized models can be effective for specific tasks, the overall trend is towards general models that incorporate learnings from specialized iterations.

The future will likely see more choices of AI models rather than a single dominant model.

Companies are increasingly using multiple AI models for different tasks, optimizing performance by selecting the best model for each specific application.

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 last two years have been dominated by large AI models, which have proven to be popular and effective, but we are now entering an era focused on complex workflows around these models.

The analogy of Transformers being the new transistors highlights how AI models can be packaged and utilized in ways that are accessible to billions, similar to the evolution of desktop and mobile interfaces.

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

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 current AI boom is largely due to the Transformer architecture, and I believe there will be future breakthroughs of similar significance, though I can't predict them.