The AI data center of the future differs significantly from traditional data centers, as it requires a more industrial approach to infrastructure, energy, and scale.

The nature of data center tasks is shifting from traditional database access to inference and training, which require thousands of GPUs to be connected in high-speed networks for efficiency.

The demand for data centers is increasing as more people want to use AI models, and we need to ensure we have enough infrastructure to support this demand.

We decided to simplify data centers by integrating all components into a single building block, making them more reliable and easier to manage, while also trying to replicate Apple's model by providing large building blocks for customers to build their own data centers, similar to how Apple built the iPhone with integrated hardware and software.

Our data centers utilize a cooling architecture that involves massive pipes moving water to cool chips directly, which is essential for managing the increased power density of modern data centers.

The future data center will be interconnected, allowing for a coherent cluster of high-performance computing resources, which will be the largest of its kind once completed.

The future of AI will require not only more data centers but also the ability to generate new data, as we are running out of existing data to train AI systems effectively.

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 demand for computing power is growing rapidly, and we can't ramp up spending fast enough to keep up with use cases.