LeCun's group published Sub-JEPA improvements to LeWorldModel, demonstrating consistent performance gains in self-supervised world modeling for embodied AI agents. The work applies joint-embedding predictive architecture principles to reduce training overhead while maintaining or improving downstream task performance. For builders deploying embodied systems, this reduces the computational cost of pre-training world models—a historically expensive bottleneck. Lower training overhead accelerates iteration cycles for robotics and simulation work, enabling teams with constrained compute budgets to develop competitive agents. The efficiency gains compress timelines from months to weeks for baseline model development. For operators, this signals predictable incremental progress in the self-supervised learning stack rather than step-change breakthroughs. Incremental improvements compound in infrastructure efficiency. Teams should expect continued refinement in world model efficiency, making real-world robotics deployment less dependent on massive pre-training budgets. This shifts competitive advantage from raw compute availability toward data quality and fine-tuning strategy.