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.).
In a way, it is the opposite. Code is what you execute. The transistors are the engines that do the execution. They are going to expect a chip they designed with 105B transistors to perform (speed/efficiency/whatever) in the same ballpark as a high=end GPU for their AI workloads.
It is like knowing the kind of engine a car has. Not all V8 gas engines produce the same power, but knowing that it is a V8 instead of an inline three cylinder does give you an idea of the expected performance characteristics.
