Traditionally, reservoir simulation has benefited from increased resolutions and incorporating more physics, hence the evolution from million cells-sized reservoir simulators, billion and now trillion cells. Instead of the traditional route this talk will discuss the future of reservoir simulation in the context of Machine Learning (ML). With examples of incorporating ML to flash calculations, ML-based well placement and intelligent adaptive mesh refinement, the talk will demonstrate where ML can have an impact on reservoir simulation while maintaining fidelity of results. The replacement of parts of the simulator, or the entire simulators, by surrogate, or proxy models that are learned from physics and run at a fraction of the computational cost could be in our near future.