We analyze how node mobility can influence the convergence time of pairwise gossip algorithms. Our main result is that even a small number of fully mobile nodes can yield a significant decrease in convergence time. We find lower bounds on the convergence time by merging nodes according to their mobility pattern and analyzing a related Markov chain. We can then show that if the agents have one-dimensional mobility in the same direction the convergence time is improved by at most a constant. Our upper bounds also use techniques from the theory of Markov chains, and show that mobility can dramatically accelerate gossip as long as the mobility paths significantly overlap.