We provide a new perspective on the procedure of handling input cost constraints for tight achievability results. In particular, we show with a proper choice of input distribution and using a change of measure technique, tight bounds can be achieved via
the common random coding argument and a modified typicality decoding. Such insights are then extended to a Gaussian multiple access channel, for which
independent uniform distributions on ``power shells'' are shown to be very close to second-order optimality.