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.

For real world robotics deployments, learning the initial procedure is only part of the challenge. Most startups' robot foundation models are already able to do that. To deploy in to the real world, robot also needs to adapt when that procedure changes. In-context learning offers a way to communicate those changes through another demonstration, rather than automatically starting a new data-collection and training project. That reduces the engineering required to keep complex deployments useful as the customer’s operation evolves. Skild had a unique focus on in-context learning, and it is reaping the rewards in the scale of their deployments within the first year.

Two and a half weeks ago Dyna Robotics published that their model is actually working, and now they are lifting the curtains on some of their deployments. Dyna-2 is a world-action model pre-trained on 1 million hours of first-person human video, which equates to training on about 170 years of hands. They unlocked transfer. With the same checkpoints, scored on robot data the model never saw, zero fine-tuning, their scaling curve holds. They hit a human-to-robot scaling law. Somewhere between 10k and 100k hours, the embodiment gap starts closing on its own. At a customer site neither has seen, Dyna-1 passes 46%. With Dyna-2 scaling successfully, it passes 87%, enabling Dyna to finally start scaling their deployments. Din Tai Fung, one of the highest revenue-per-location chains in the US, is rolling Dyna robots across its network, with hundreds of robots by H1 2027.

Excited to be on the Business Insider list of top robotics investors. The data wall is falling. For years, the bottleneck was teleoperation: slow, expensive, narrow. Now robots can learn from human video, practice in simulation before touching reality, and there is enough funding to scale data deployments. Robotics is finally running the playbook that took LLMs from autocomplete to reasoning. There are still many open frontiers. General manipulation. The right API layer for the robotics models. Application robotics. That's exactly why I'm bullish. We've just found a recipe that works. Thanks to @ryajetha for compiling the list.

Excited to have backed @reflection_ai from the seed. Today’s $2b raise pushes frontier open intelligence, with open models + advanced RL at scale, accessible to all. And most importantly, with @nvidia as their key partner. It’s a big day for open AI. Thanks NYTimes for covering