Random number generators commonly found in default libraries are actually pseudo-random number generators. Nevertheless, more sophisticated options exist to gather entropy from "truly"-random sources such as atmospheric noise, thermal noise, electromagnetic and quantum phenomena.
Speed of random number generation is important, as for instance Monte Carlo simulations need a large amount of random numbers. Since speed can be the bottleneck and Monte Carlo simulations are used in many fields, an improvement in random number generation speed translates into real-life value generated.
What about the quality of the randomness? Are there real-life applications in which the random number generation quality is the bottleneck? That is, are there situations in which an improvement in quality (or generation cost for a given quality) of random-number generation over the state of the art would result in real-life value generated?