The discovery in 1998 of a link between the Wasserstein-2 metric, entropy, and the heat equation, by Jordan, Kinderlehrer, and Otto, precipitated the increasing relevance of optimal mass transport in the evolving theory of finite-time thermodynamics, aka stochastic energetics. Specifically, dissipation in finite-time thermodynamic transitions for Langevin models of colloidal particles can be measured in terms of the Wasserstein length of trajectories. This enabling new insight has led to quantifying power and efficiency of thermodynamic cycles that supersede classical quasi-static Carnot engine concepts that alternate their contact between heat baths of different temperatures. Indeed, naturally occurring processes often harvest energy from temperature or chemical gradients, where the enabling mechanism responsible for transduction of energy relies on non-equilibrium steady states and finite-time cycling. Optimal mass transport provides the geometric structure of the manifold of thermodynamic states for studying energy harvesting mechanisms. In this, dissipation and work output can be expressed as path and area integrals, and fundamental limitations on power and eficiency, in geometric terms leading to isoperimetric problems. The analysis presented provides guiding principles for building autonomous engines that extract work from thermal or chemical anisotropy in the environment.