More than a year ago, we released DYNA1, “the first robot foundation model built for round-the-clock, high-throughput dexterous autonomy.” Looking back at the DYNA1 videos now, it’s funny how we thought of it as “high-throughput dexterous” back then. With DYNA1, we showed for the first time that a robot foundation model could be robust enough for true 24/7 automation. But it still wasn’t enough to fully meet customer needs — customers care about throughput, and they care about quality of outcome too (they even want us to remove napkins with stains on them...) It’s hard, and some people may see napkin folding as a “small” use case. But we thought it was important to go through the full deployment cycle to figure out what the real research problems in robotics are. Through the DTF deployment, we explored large-scale pretraining, RL, data-collection hardware, and a lot more. So here it is: a real “round-the-clock, high-throughput dexterous autonomy” system that creates value for customers. If you care about solving the real problems in robotics, join us!

This is our release that I am actually most excited about because it shows and proves what truly matters. Deployment is the ultimate prize and the only reliable eval for robotics. After having worked on so many research projects and models in my career, where I saw so many new capabilities I had never seen before, there was always a question in the back of my mind: do they matter and how? At a time in robotics where the signal-to-noise ratio is so low, where there’s so many demos, models, pilots, you see everyday, the conclusion I arrived at has always been shipping our models, robots, and entire systems to real customers and proving that they actually fulfill a need for real people. Showing a demo is easy, proving that the robots create sustainable value for customers over a long period of time is extremely hard. We know it's hard, but we also know it's necessary to get right. This mindset is also why I decided to start Dyna with @Lindon_Gao and @YorkYang5050 in the first place, because our heart has been focusing on the right problems from day 1. It’s great to see the whole team’s effort culminating in this first scaled deployment release of its kind for the industry. But we are just getting started, and I am more excited about the future of the physical economy than ever before.

I started working in robotics a little over a year and a half ago, and it still humbles me every day. The physical world is just hard. There is no undo button. Given enough time, every weakness—in the model, hardware, data, infrastructure, deployment, or operations - eventually shows up in the customer’s workflow. Success takes an incredible team working across the entire stack. The goal is to stay at the research frontier while delivering real economic value at scale - deployments are the true eval. Incredibly proud of what the team at Dyna has accomplished. We're solving a problem that's still wide open. DM me or check out if you want to help build what comes next.

Most Physical AI companies are still doing lab demos. A key factor in our investment in Dyna last year was their world class post training expertise/results and their deployment focus. More deployments begets better data begets better models begets faster time to deployment We saw this loop play out for multimodal models like ChatGPT/Claude and autonomous driving and we're seeing it play out for robotics

The heroes behind deployment are often underrated. A strong, general model is not enough to get robots into production. Anyone who has really deployed knows that most of the important lessons only show up in the field — and you need serious infrastructure to capture them. At Dyna, we’ve spent a lot of time building observability, auto-labeling, feedback, and evaluation systems that turn deployment into a continuous learning loop, with humans guiding the loop where they add the most value. That loop is critical. It tells us where models actually fail, what needs to be solved fundamentally, and where our foundational research should go next — instead of patching problems one deployment at a time. Deployment isn’t just a business use case for us. It’s what powers the data and feedback flywheel behind scalable robotics. More here: There’s probably a lot more behind it than you’d expect.

Over the last 2 years we went from “This AI thing is pretty cool but buggy” to “This is incredible we need to be tokenmaxxing or get left behind” We are entering that transition phase for robotics now

We’ve seen plenty of impressive robotics demos. But now the bar must move higher: robots must create real value in the real world — and that value must scale. For us, the real test is not whether a robot can complete a task once. It is whether customers get enough value that they want to deploy more. Robotics only matters when it solves real problems: taking repetitive, tedious, dirty, and difficult work off people’s hands while creating meaningful economic value for the businesses using it. ROI and scalability are inseparable. One successful deployment can prove customer ROI, but not a scalable product or business. If every new customer or workflow requires rebuilding the solution, the economics will never scale. You only prove that ROI can scale when the same underlying technology keeps creating value across customers, workflows, and industries — while the effort, cost, and time for each new deployment keep coming down. We think about the path very simply: Demo: “It works.” You’ve proven technical possibility. Pilot: “It works here.” You’ve proven it can work in a real environment and start creating value. Scaled deployment: “We want more.” Customers keep expanding because the ROI works, and we can keep delivering that value without rebuilding everything from scratch. Every deployment must make the next one better. A problem solved in the field should leave something reusable behind — a better model, better tooling, better infrastructure, or a more general capability. What we learn from one customer should make the next deployment easier, faster, and more reliable. This is also why research and deployment must stay tightly connected. Research expands what robots can do. Deployment tells us what actually matters, where things break, and what must be solved fundamentally — not patched case by case. You need both to build a product that can truly scale. Today, we’re sharing more of what we’ve learned across a broad range of industry partners — the successes, failures, operational challenges, and hard-earned lessons behind getting robots to create real value in production. The goal is not just to make robots work. They must create real value. That value must repeat. And it must scale.

Oh hey, measuring progress in robotics is actually really easy. Put them on customer sites running autonomously and let the market tell you if your stuff works and makes economic sense. That's as real as a signal gets. Let the world grade your models. Everything else is a proxy.

After so many demos, models, pilots, robots are still struggling to land real deployments with real customers. Until now. Today we’re excited to share that our robots have successfully crossed the ROI threshold, and Din Tai Fung, one of the highest revenue per location restaurant chain in the US, is rolling out Dyna robots across its extensive restaurant network. This brings our rollouts across hotels, logistics, data centers, and many other use cases to a fleet that reaches hundreds of robots by the first half of 2027. And we’re just getting started. It’s been a wild year, and today we’re double clicking on the battlefield stories and sharing a few learnings about scaling robot deployments. We are just scratching the surface. Read the full blog post:

Many Thursdays ago, I got this text: “Understand u guys r quite tied up with existing deployments, but when can we get our next batch of robots?” I read it three times. Anyone who has built something from 0 to 1 knows the feeling. For months, you’re pushing a boulder uphill. You don’t know if there’s a top. Then one day, almost quietly, you feel the boulder start rolling on its own. The question had changed from “does this work?” to “when can we get more?” We found our fit. The path there was not obvious. For a long time, the question I got most about Dyna was: are you a model company or a deployment company? What I rarely admitted was that I wasn’t entirely sure either. We knew what we were doing, but it felt like we were swimming against the tide because investors kept pushing us to pick a lane. The cleaner path was to just build the brain. It was a story with no revenue pressure, software-like scalability, better multiples, and less messiness. But the real world is brittle, and robots are not LLMs. So we kept coming back to what we were actually here to build. The answer wasn’t another breakthrough model. And it wasn’t more unscalable deployments. It was a robot people wanted. It took us a while to realize there was a third path. Frontier research makes the product possible. Deployment teaches us what the research needs to solve next. Each makes the other better. Both are necessary to build a PRODUCT people want. After all, isn’t that why we’re building robots in the first place? We are Dyna. A product company that turns frontier intelligence into real-world impact. There’s still more to come.

Wow, great product. Thoughtful design, a clever pick function that makes it genuinely useful, sim training that turns play into a real development platform, and just enough personality. Hope this brings a lot more people into robotics. I’m buying one. Congrats @ClementDelangue and the @huggingface team!