Abstract-Contrary to the common use of random coding and typicality decoding for the achievability proofs in information theory, the tightest achievable rates for point-to-point Gaussian channels build either on geometric arguments or composite hypothesis testing, for which direct generalization to multi-user settings appears challenging. In this paper, 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. It is observed that a codebook generated randomly according to a uniform distribution on the "power shell" is optimal, at least up to the second order. 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 optimal, at least up to second order.