LeCun isn’t inventing world models from scratch—Schmidhuber and Sutton laid that groundwork decades ago. Schmidhuber’s 1990 paper “Making the World Differentiable” taught an RNN to predict its environment and trained an agent inside that “dream.” Sutton’s 1991 Dyna architecture combined learning, planning, and reacting by imagining futures before acting. Ha and Schmidhuber’s 2018 World Models built a VAE-plus-RNN pipeline to race cars purely in latent space. LeCun’s 2022 JEPA proposal and his new LeWorldModel paper sharpen the focus: don’t predict raw pixels or tokens, predict abstract representations that capture only what matters. LeWorldModel tackles the collapse problem—where latent-space predictors map everything to one point—by introducing SIGReg (Sketched Isotropic Gaussian Regularization). Instead of a dozen heuristics and hyperparameters, SIGReg projects the batch of 192-dimensional embeddings onto random lines and runs a one-dimensional normality test. Pushing those projections toward a Gaussian keeps the full distribution well-spread. The training loss is simply L_pred (MSE between predicted and actual next latents) plus λ·SIGReg, with λ found by a quick bisection search. The result: a 15 million-parameter model (5 M for a ViT-Tiny encoder, 10 M for a Transformer) that trains on one GPU in hours, avoiding stop-gradients, teacher networks or pretrained backbones. At inference, it uses Cross-Entropy Method planning entirely in latent space. You encode start and goal images, sample action sequences, roll them forward, score by distance in latent space, and resample. That algorithmic simplicity yields a 48× speedup over DINO-WM’s 47-second planning time—LeWorldModel does it in about one second thanks to radical compression. Validation comes from two angles: supervised probes reading out positions and angles from the latents, and violation-of-expectation tests showing higher “surprise” for physically impossible events. Those results suggest the 192-dimensional vectors encode real structure, not arbitrary noise.