What makes you think that models will improve with the same pace that they have been improving in the past few years?

A few reasons: -2.5 years is a pretty short time for a new tech development, even if it fails eventually - usually when a new tech is introduced, the biggest gains happen when the environment is changed to fit it. That takes time: libraries, api's, verification tooling, rl environments, skilling users, etc. - possibility of orders of magnitude hardware cost reduction - Optical. Analog. Rram. Many others. Something will work. And internal improvements in the model architecture. And there's scaling in reasoning time.