Skild's S1 stands out because it showed more pretraining data made the model substantially better at performing unseen tasks from a single video prompt, without fine-tuning. A stronger foundation model made each new demonstration more useful. Now, Skild is showing a complementary approach: generating experience that humans never have to demonstrate. Skild post-trained a soccer policy through 140 years of simulated self-play against earlier versions of itself. No human demonstrations during this post-training phase. Behaviors like shielding the ball and tackling emerged without separate rewards engineered for each skill. The policy then transferred to a physical humanoid. AlphaGo was such an important moment for AI because human expertise could be a starting point rather than the ceiling. A system could discover strategies nobody had explicitly taught it. Self-play creates a powerful learning loop. As the model improves, it becomes a tougher opponent for its next version. Progress generates the next challenge. More compute can produce new experience, rather than just more training on a fixed dataset. What’s interesting here is applying it on top of a general robotics foundation model, with behaviors transferring into the physical world. Together, Skild’s results suggest two complementary ways to scale: learn more effectively from the experience humans provide, then improve through experience the model generates itself. Soccer has clear objectives and measurable outcomes. Extending this to everyday work, and showing that the gains generalize beyond a single task, is the next test. Skild is showing how a capable foundation model could help change that constraint of human capability: human demonstrations get it started, without having to supply every subsequent improvement.