Umm, Amdahl's law is actually a negative result. It's saying that if you infinitely improve some magical hotspot that is 1% of your workload, the best you can only ever get to is 99%. If anything, Amdahl is telling us not to grind on trivialities.
As a real world example, if an operation involves a network call and you see the RTT dominating the time. You may want to think of ways to avoid the call (caching etc..) if possible to get really good gains.
But I'm an EE, so maybe I misunderstood some finer points?
Amdahl:
A fairly obvious conclusion which can be drawn at this
point is that the effort expended on achieving high
parallel processing rates is wasted unless it is
accompanied by achievements in sequential processing
rates of very nearly the same magnitude.
A point made when we read the paper was that Seymour Cray always made sure that his computers were also the fastest scalar computers even though they were sold as vector processors.
I think mathematicians consider an upper limit a positive bound - positive, in the sense of being well defined; you're using negative in the other sense? I actually like that quite a bit.