When Prediction Chooses the Representation: Learning Dynamics and Planning in World Models

World models are trained to predict but deployed to make decisions. What happens between these goals when an encoder and latent dynamics model learn together? Predictive training creates a competition: easy-to-predict features gain capacity early, accelerate their own dynamics learning, and suppress harder features that determine which actions succeed. When selection varies across the state space, it bends latent geometry, changing which trajectories appear near or inexpensive to a planner. A model can thus predict accurately and avoid collapse while presenting the wrong world for decision making. Constraints on marginal latent distributions cannot rule out this failure, because the same healthy statistics can encode controllable structure or irrelevant nuisance. Our analysis reveals the missing design object: the geometry of action-conditioned consequences. Useful representations must preserve distinctions that change attainable futures, identify variations that do not, and stop changing once this decision geometry is adequate. This connection between learning dynamics and planning quality turns diagnosis into model-design guidance. Latent capacity should protect decision directions; training should prevent predictable nuisances from starving them; poor action coverage should prompt informative interaction rather than stronger regularization; and constraints should become silent after forming planning-compatible geometry. Evaluation must therefore look beyond prediction loss and latent health to ask whether the learned geometry preserves decisions. A world model should not merely predict the world, but organize it around the decisions it will face.