You can chuck as much RAM as you like at your problem but it's not going to help once your data set fits in memory - and maybe way before that if you're thrashing those piddly L3 and L2 cache's.
And that's why knowing your hardware and your workload is important. I remember once we had a performance problem with a x86 server. Over lunch, the guy with the problem told me the working dataset was about 8 megs per batch or so. I told him to move the workload to an Itanium server we had for testing because I knew that server had 11 megs of L2 cache, besides being slightly faster per instruction than the Xeon that was running the workload. The throughput more than tripled.
It breaks my heart when people just assume more memory/GHz/cores is always faster.
Are you perhaps missing that it's a deliberate inversion of the more obvious statement "needing more RAM is for people who don't know how to use Algorithms" in order to make the point that RAM is cheaper than an engineer's time?