Strong scaling laws for one-shot learning in long-horizon manipulation tasks. It's only a matter of time until these models are ready for widespread deployment with very little task-specific data.

Tomorrow.

Training the model is only half of the work. Excited for Skild to continue scaling up deployments

We just hit 100M ARR within 10 months of starting deployments. We are in factory lines. On construction sites. In kitchens. In data centers. Cleaning. Welding. Building. Cooking. Deploying.

November 30, 2022: “The ChatGPT moment” August 25, 2026: “The Skild S1 moment” It’s been wild to witness this up close.

This is pretty insane. It’s been a crazy few months for physical ai, and this is one of the most cracked videos I’ve seen yet 🤯🤯

In-context learning for robotics is here. - Long-horizon tasks over 10 minutes long - Never seen during pre-training - Prompted with one video, no fine-tuning We are building intelligence from the foundations up, not from the top down.

Show it a single human demonstration of a long-horizon task and the robot performs it right away. No fine-tuning, no retraining. Skild has released S1, a robot foundation model where you specify the task with a video demonstration instead of a language instruction. Why video, not language Language works for "hand me the mug." It falls apart for anything delicate or long-horizon. You don't learn to fold a fitted sheet from a sentence. So S1 takes a video demo into its context window and translates it to its own body and scene. What it does - Tasks up to 10 minutes, dozens of sequential steps - Unseen tasks: potting plants, pour-over coffee, kit assembly, pancakes. Skild says even the underlying primitives, like flipping a pancake, weren't in pre-training - Recovers from its own mistakes without being trained to - Improves on flawed demos. Demonstrator drops an egg, S1 doesn't make that mistake. It reads the demo as a goal, not a trajectory to copy.

We have acquired Zebra Technologies’ robotics arm (formerly Fetch Robotics). This is what happens when orchestration meets intelligence -- a major step toward fully autonomous warehouses. More robots. More environments. One unified brain.

We hosted Prof. Alyosha Efros (UC Berkeley) at @SkildAI! He didn't believe that robots could actually cook eggs reliably. :) Tested back-to-back 5times without fail! One batch of scrambled eggs every ~2.5mins nonstop. The same model assembles a GPU on a server rack too.

Robotics is about to have its LLM moment. One of the big issues with industrial robots is having to program individual tasks down to the millimeter and hope nothing changes. Now imagine a foundation model that controls any robot for any task, learning from data rather than being manually programmed. This could become a flywheel where the more we use the robots, the more data we generate, and the better the robots become. We have no idea what's coming.

Deep learning for manufacturing at @SkildAI - much more robust and scalable than classical robotics

Nearly every system today, from energy to chips to food, is bottlenecked by scarce human capital. We are changing that by building AI-powered industries of the future. Check out Skild Brain robustly assembling GPU racks, a highly precise task, live at #NvidiaGTC.

Robotics is a data problem. Today, we’re partnering with @ABBRobotics, @Universal_Robot, and @NVIDIARobotics to deploy the Skild Brain across real-world industries from manufacturing to factory lines. This will help us build the world’s biggest data flywheel for physical AI.

India, we’re here! 🇮🇳🇺🇸 From one Silicon Valley to another, we’re thrilled to open our newest office in Bengaluru.

We keep saying China is on a collision course with the US. But what if China is not heading for war, but running a campaign? In a new article, Todd Hall and I argue China acts as a global opposition party, using institutions, coalitions, and messaging to win influence. Link👇

Announcing Series C We’ve raised $1.4B, valuing the company at over $14B With this capital, we will accelerate our mission to build omni-bodied intelligence 🚀 skild.ai/blogs/series-c

Let robots clean in peace 🧺🤖✨

Superhuman cooking AI achieved (under generous assumptions), by learning from video the way humans do

Humans learn by watching. Robots should too.