Skild, Generalist, and Sunday published their charts inside five weeks, allowing us to finally measure the progress of robotics foundation models objectively. GPT-3 mattered because in-context learning showed up: put examples in the prompt, get tasks the model never trained on. Sunday's ACT-2 folds laundry at 99.1% in homes it's never seen. But it's one task the lab picked, and the curve is squeezing against its ceiling. It proves scaling perfects for only a chosen skill. Generalist's GEN-1.5: loss falling through 8 months of training, and 59% one-shot success on 10 novel tasks. These results are impressive. But the loss chart tracks prediction error, not task success. And 59% is a single measurement at a single scale. One point can't show you whether scaling is working. Skild tests their robotics model on the most accurate benchmark. In-context learning claims only mean something relative to how far the eval sits from training. They put that distance on the exam, and scale on the x-axis. By that benchmark, the Skild S1 stands alone. On unseen tasks, one video prompt, zero fine-tuning, they are at ~0% at 1k hours. 7% at 10k. 25% at 30k. 66% at 100k. Convex, still bending up. In-context learning should be graded against task unseen and is it long horizon. S1 clears both, using ten minute tasks with dozens of steps that never appear in training. Skild will be sharing more about how they achieved these results soon.👀