We build our entire system full stack, and then we build it end-to-end at data center scale but then when we go to market, we disaggregate this entir… - Jensen Huang

We already work very deeply with end users and developers who do these things. We do that today and our engagement with the world’s leading important verticals that we focus on, whether it’s healthcare, automotive, of course all the AI startups, we work with some 10,000 AI startups. So industry after industry, if there are industries where we could add a lot of value, the video game industry, we have direct coverage on just about every developer. The automotive industry, we have direct coverage on just about every single car company. The healthcare industry, we’re working with just about every drug discovery company and so we already do that today. It’s just that the fulfillment of the system ultimately comes from somebody else. If you want your stack accelerated, you work with Nvidia.

The computing fabric that compute connects processors needs to be quite high speed. The faster the processors, the greater need for high speed computing fabrics and so it’s a matter of scale and the effectiveness of the scale. For example, if you want to increase to 1000 processors, the linearity of that scale up would be less linear and it would plateau earlier if the interconnects were slower and so that’s basically the trade-off. It’s just a matter of how far can you scale and what is the effectiveness of the scaling, the linearity of the scaling.

You could learn the language of almost anything. Once you learn the language, you can apply the language - and the application of language is generation.