AI Supremacy Has embodied AI and robotics hit a milestone in 2026?
Good Evening,
Visions of AI is a new feature format I’m experimenting with that will amount to a short profile on an AI or emerging tech startup. The cadence of this style of article is unknown as of yet, but there are a lot of fascinating startups I want to discuss and share about.
This is designed to be light evening reading to go out at a time-slot of 8 pm EST.
In late 2026 there’s been a very sudden spike in massive Physical AI and embodied AI funding rounds, especially in the software around the robotic brain, and humanoid robotics, a form factor of bipedal (human-like) robots. While it remains to be seen how significant or enduring this trend is, it follows a flurry of Chinese robotics companies going public. China is widely seen as the robotics leader of the world, especially in current sales, hardware and manufacturing.
About three weeks ago a Physical AI startup called Skild AI released their S1, an in-context learning model for robotics. Skild AI has hit over $100 million1 in revenue already in 2026. The company claims that S1 new is a foundation model that learns from one example (one-shot learning). They said that it can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning.
Embodied AI with a Real Revenue Ramp (Skild)
After doing some digging while it’s a marketing idea (being pushed by the industry), I do believe S1 could qualify as the GPT-3 moment for robotics learning, at least in its 2027 iterations. In the history of AI, that would correspond to around 2020. Generative Pre-trained Transformer 3 (GPT-3) was a landmark 175-billion parameter LLM developed by OpenAI where the research paper introducing it was published on May 20th, 2020, about 3.5 years before the ChatGPT moment itself.
This is tremendously exciting for robotics and Physical AI enthusiasts.
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Key Terms Explained #
Fine-Tuning The process of taking a pre-trained model and continuing to train it on a smaller, specific dataset to adapt it for a particular task or domain.
Foundation Model A large AI model trained on broad data that can be adapted for many different tasks.
GPT Generative Pre-trained Transformer.
In-Context Learning A model's ability to learn new tasks simply from examples provided in the prompt, without any weight updates.