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.

The robotics companies that win won’t just have the best demos. They’ll have the strongest deployment loop. Skild AI: first commercial deployment → 60+ paying customers → $100M ARR in 10 months. Every deployment creates new problems to solve and new knowledge to feed back into the system.

The best place to learn isn't in the lab. It's in the real world, in actual deployments with actual customers. Skild has shown off their impressive tech before, but they're now also showing off their impressive commercial traction and have already passed 100M ARR. If we haven't reached the "ChatGPT moment for physical AI" yet, we're surely close.

Skild‘s physical RSI(recursive self-improvement) ->Start with a foundation model that learns across robots, tasks and embodiments. ->Deploy it across real-world applications. Each deployment adapts to the task, and production data makes it better. ->Feed that experience back into the general model. Every deployment makes the next one smarter. full:

@SkildAI's new S1 robot foundation model helps robots learn previously unseen tasks from a single video demonstration. 🤖 See how NVIDIA technology supports S1 from training and simulation to real-world deployment. Learn more ➡️

Stop staring at the $100M. I don't know if people really understand what @SkildAI just did... They crossed $100M in ARR only 10 months after their first commercial deployment. 10 months. 60+ paying customers. AND? Robots are already moving goods, preparing food, inspecting sites and working inside factories, warehouses and data centers. But this is much bigger than a revenue milestone. Skild is showing what happens when you treat DEPLOYMENT as part of the AI training process. A robot can look perfect in a demo and still be useless in the real world. What?? 99.9% accuracy means nothing if the robot is 10x too slow. And once you put robots into actual factories, everything keeps changing... New parts, layouts, processes. That's where deployment becomes incredibly valuable. Every robot in the field encounters new situations, failures and edge cases. That experience can flow back into the general model and make the next generation better. Think about the flywheel... Train one general robot brain. Deploy it across dozens of different real-world jobs. Learn from what happens. Feed that experience back into the model. Then send a better robot brain into the next deployment. This is massive! Because suddenly every deployed robot isn't just doing work. It's also helping improve the intelligence behind every future robot. You can already see this influencing Skild's technology: Their new S1 model can learn a completely new task from a single video example without fine-tuning. Show the robot what to do, AND... it can adapt. For factories where parts, layouts and processes constantly change, that's a huge deal. Skild is already working with @nvidia and Foxconn on Blackwell assembly, Sumitomo Wiring Systems on wire harness manufacturing and Mitsui on commercial kitchens. The robotics companies with the biggest real-world deployment fleets could eventually have one of the biggest advantages in building better robot models. Because the real world keeps teaching them. @deepakpathak and @gupta_abhinav_ put it perfectly: “The era of demos is over; the era of deployment has begun.” —— Weekly robotics and AI insights. Subscribe free:

Skild just crossed a $100M annual revenue run rate, ten months after its first commercial deployment. It now has 60+ paying customers and is working with NVIDIA and Foxconn to deploy robot arms for Blackwell system assembly. Those deployments helped shape S1. Customers keep changing parts, layouts and workflows. Having to collect data and retrain every time gets expensive fast. S1 lets operators teach a new task with a video prompt, without updating the model’s weights. It addresses a practical problem: keeping robots useful as the work changes. Skild’s bigger bet is that experience from different customers can improve the general model, so future deployments need less customization. The business grows by deploying robots; those deployments help build the next version of the brain. Congrats to @deepakpathak and the whole team!

ROBOTICS STARTUP SKILD AI HITS $100M REVENUE RUN RATE Skild AI has reached a $100M recurring revenue run rate just 10 months after beginning commercial deployments. The company’s general-purpose AI “brain” is now: • Deployed across hundreds of robots • Used by 60+ companies, up from just 8 earlier this year • Running across warehouses, factories, hospitals, homes and cafes • Compatible with humanoids, robotic arms, hands and quadrupeds Skild was valued at $14B in January after raising $1.4B and has previously partnered with $NVDA and Foxconn on robotics automation at a Houston chip factory. Its new S1 model can also teach humanoid robots multistep tasks by having them watch human demonstrations, including tasks lasting more than 10 minutes. Skild says the growing deployment base is creating a data flywheel: more robots deployed → more real-world data → better models → more deployments. Source: Bloomberg

Having spent years building robots and deploying hundreds of systems in the real world, I know how hard it is. Huge congrats, a really impressive milestone, great to see robots getting out there and doing real work!

Robotics is moving from the era of demos to the era of deployment. @SkildAI just crossed $100M ARR with 60+ paying customers and only 10 months after starting deployments !! And every deployment makes the intelligence better. The real-world learning loop is the breakthrough. Huge momentum @deepakpathak @gupta_abhinav_ 🦾 We at @felicis are proud true believers in Skild AI !! cc @DetweilerJames

The best thing you’ll read all day. Something @deepakpathak and @gupta_abhinav_ have always stressed at Skild is that not only is research a necessity to develop the best deployment solutions, but deployment is a necessity to contextualize, evaluate, and guide research developments. In robotics, the two must work hand in hand to be most effective. The whole reason for moving from academic research to industry is to accelerate that feedback loop between research and deployment. A method developed in the lab isn’t good enough until it’s proven in the real world. That’s the whole point of research, to develop solutions to problems that actually matter, and unlike digital agents, capabilities of physical agents cannot easily be verified in isolation. We believe progress towards making robots a viable solution means getting your hands dirty with real deployments in real world environments for real customers. Doing so often involves a fair share of grunt work and a different level of discipline that turns off many researchers. It’s easy to feel like the work is done and it’s time to move onto the next cool dexterous capability once you’re able to shoot a video of the result. It’s an entirely different mentality when 1 failure out of 1000+ trials is enough to keep you up at night.

A big milestone for the @SkildAI team as the company crosses $100M ARR! Congratulations to co-founders @deepakpathak, @gupta_abhinav_, and the entire Skild AI team! It’s been incredible to see the team’s focus on bringing intelligence into the physical world. Skild AI continues to be one of the fastest-growing robotics companies, a remarkable example of how quickly AI is moving beyond the era of demos and into deployment. CC: @ravi_lsvp, @ravirajjain

The era of intelligent robot deployment is here. Congratulations to @deepakpathak , @gupta_abhinav_ , and the entire @SkildAI team on crossing $100M in revenue run-rate. It's easier to show demos, but deployment in real-world environments requires a LOT more - meeting strict SLAs, consistency of performance, robustness to perturbations, durability, and more. When the ability to adapt to new stimuli and environments is critical, S1’s in-context learning can enable robots to learn new tasks rapidly. Truly bringing AI into the physical world! Skild AI is not just innovating on the underlying RFM (robot foundation model) but also in bringing this revolutionary technology to 60+ customers at light speed :)

💥 The era of demos is over. The era of deployments has begun 3 yrs in, 10 mo after 1st deployment: 60+ customers, $100M in rev run rate 🤖 @nvidia and Foxconn for high-precision assembly of Blackwell systems 🤖 Sumitomo to automate wire-harness processes previously considered “impossible” to automate 🤖 Mitsui to operate commercial kitchens serving supply chains for 1.4 million meals a day Real world applications!