In a world with AI, we need hardware and software that are more aware of their environment and can interact in ways beyond just typing and screens.

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.

The future of computing will involve running incredibly large neural networks with high performance and low power consumption, requiring different solutions for training and inference workloads.

The adaptation of compute platforms is essential for running capable models in low power environments, such as smartphones and autonomous vehicles, as well as in large-scale data centers.

The capabilities of AI are rapidly evolving, leading to increased demand for data centers and chips, contrary to expectations that efficiency would reduce these needs.

The AI models you're using today are the worst you'll ever use for the rest of your life. Every two months, computers can do something they've never been able to do before, so you need to think differently about what you're doing.

There's a significant gap between the current compute capabilities and what could be achieved with ten to a hundred times more compute, which would allow for much greater AI services.

Both algorithmic improvements and computational advancements, such as better chips and larger data centers, are essential for progress in AI.

The long-term differentiation in AI will not be in the base models but in how personalized and integrated they are into users' lives, providing context and utility that meets individual needs.