VaViM and VaVAM introduce a novel approach to autonomous driving using large-scale generative video models. VaViM predicts video frames through autoregressive modeling, while VaVAM generates driving trajectories via imitation learning, showcasing emergent behaviors in complex driving scenarios. The paper analyzes the model's performance, including its strengths and limitations in various driving situations.