Google may spend about $500,000,000 / year on servers [educated guess, probably within a factor of 2]. More than the cost of 1000 engineers. It's worth it for them to put serious effort into efficiency.
Perhaps their bottlenecks are mostly in bandwidth, memory and disk space? Otherwise, I'm very surprised Google doesn't invest A LOT more into languages and VMs.
Bisection bandwidth is likely the most scarce resource in their environment, with memory following. Not sure on how disk and compute would rank. Architecture is the most important tool for optimizing cost on such systems, which is why they put effort into things like bigtable and map/reduce. Language efficiency does matter when you're buying servers by the truckload, but it's impact on cost is linear, whereas mistakes in architecture could be much worse.
