GPUs (and AI chips) are highly parallel, containing thousands upon thousands of the same compute units. The performance of these chips is very much dependent on

GPUs (and AI chips) are highly parallel, containing thousands upon thousands of the same compute units. The performance of these chips is very much dependent on having a sheer number of transistors to form into as many compute units as possible. If we assume that Microsoft is roughly able to architect compute units of a similar performance-to-number-of-transistors ratio as nVidia is, then having twice the number of transistors should roughly result in twice the performance. That is very different than it is with typical software. If you give a programmer who needs to write 100 lines of code to solve a given problem 100 more lines to fill, he won't simply be able to copy-paste his 100 lines another time and by that action be twice as fast at solving whatever problem you tasked him with. With GPU compute units, such copy-pasting of compute units is exactly what's being done (at least until you hit the limits of other resources such as management units, memory bandwidth etc.).