First I suggest you to read the original article "Reevaluating Amdahl's Law" (CACM'1988) by John L. Gustafson when you have difficulty with the wiki article. Notice that I will use the notations from the original article: $s$ for serial time; $p$ for parallel time; $N$ for number of processors.
In the original article, the author mentioned that Amdahl's Law is based on an unrealistic assumption that "$p$ is independent of $N$", where $p$ is the amount of time spent (by a serial processor) on parts of the program that can be done in parallel.
Then, the author pointed out two key observations:
- In practice, the problem size scales with the number of processors.
- As a first approximation, we have found that it is the parallel or vector part of a program that scales with the problem size.
More explicitly, times for vector startup, program loading, serial bottlenecks and I/O that make up the $s$ (serial) component of the run do not grow with problem size; however, the amount of work that can be done in parallel varies linearly with the number of processors.
As a result, as $N$ (the number of processors; together with the problem size) increases, the portion of parallel time ($p$) increases according to the two observations given above, and the portion ($\alpha$) of serial time ($s$) diminishes (in most/common cases).