The most compelling generative AI applications will emerge from innovative uses of technology that differ from traditional software applications, creating new business opportunities.

The rise of generative AI presents opportunities to create deep, complex applications that can provide long-term value, unlike simpler applications that may not be defensible in the long run.

Generative models have evolved significantly, moving from basic attempts at generation to real-time applications that can create impressive outputs, such as artistic style transfer.

General-purpose technologies like supervised learning and generative AI are valuable for a wide range of applications, and there is still much work to be done in identifying and executing concrete use cases.

The invention of CG (C for Graphics) programming paradigm opened new avenues for using GPUs beyond gaming, leading to breakthroughs in general-purpose computing.

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.

Generative AI is revolutionizing workflows and products at Meta, enhancing capabilities and creating new opportunities for content generation.

The future of operating systems may involve less traditional code, relying more on generative models that blur the lines between deterministic and non-deterministic systems.

The emergence of generative AI has led to more collaborative and expansive applications, requiring new approaches to testing and evaluation.