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 tech stack must be reimagined from first principles to accommodate new AI workloads.

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 advancements in AI depend significantly on the amount of computing power we allocate and the timing of unpredictable breakthroughs.

I think the world needs more AI infrastructure, and as we improve our technology and reduce costs, demand for AI will increase significantly.

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 user experience for AI workflows is evolving from chat interfaces to more integrated systems that run in the background, with a focus on monitoring and performance guarantees.

The bottleneck in scaling AI systems can vary, but optimizing code design can help adapt workloads to the existing infrastructure, balancing resource demands effectively.

The future of AI may involve new infrastructure and development tools that leverage the unique characteristics of AI models, differentiating them from traditional functions.