The analogy with IBM mainframe completely ignores Murphy's law which came up and lead to the small and fast chips we have today. But Murphy's law is dead. No f

The analogy with IBM mainframe completely ignores Murphy's law which came up and lead to the small and fast chips we have today. But Murphy's law is dead. No future chip will leapfrog easily current chips because we have reached hard phyical limits in chip density and downsizing. Huang's law by Jensen Huang focuses on something else and that is token performance per Watt at scale. Blackwell needs double TDP than Hopper and Rubin again needs almost double TDP on a rack but in the end Rubin will be like 100x token performance per watt on a scaled data center. This means you have more energy need but you get multiples of token performance because you start scaling in the data center. The local chip will never be able to keep up with the data center scaling economics. This is why everyone is so crazy about building data centers because they can see the economocs behind it. What people don't seem to understand if tokens become more available and cheaper then not only more people can use them but a single person can use more as well. Why should you be limited to 1 AI agent? Why can't have you have multiple agents running on multiple devices daily for you? This is why demand will grow exponentially with the growth of token economics. We have seen it for the last few years and much more is yet to come.