When we released Dyna-2 at the beginning of August, one of the most common reactions was: why? So what? Fair question. People are tired of PR releases saying “we trained on XXX hours of data” while the robots still aren’t doing meaningful work in the real world or creating enough ROI for customers. We deeply understand that. Technology is ultimately just a way to make people’s lives better, and behind every technology there needs to be a clear why. The goal of Dyna-2 was never to pile up data for attention. The goal was to build a System-1 model that can understand high-level instructions and execute them end to end in the physical world. Embodied AI has been stuck on isolated tasks for a very long time. But when our BD team talks to customers, the ask is actually very consistent: give me something that requires little to no babysitting, so humans can spend their time on what humans are actually good at. If a robot can only do one isolated task at a time, humans still need to coordinate around it, move things between steps, handle exceptions, and constantly supervise it. At that point, the robot hasn’t removed babysitting. It has just changed who is babysitting whom. That completely defeats the purpose of embodied AI, and that is definitely not the world we want to build. So we trained our foundational world-action model on 1M+ hours of human video and language supervision. The point was never that “1M hours” sounds impressive. The point was to let the model understand what people want, understand how the physical world evolves, and turn an instruction into a sequence of actions that actually finishes the job. That is the why behind Dyna-2. Today, Dyna-2.1 is carried by Taku to turn that intelligence into an end-to-end physical agent that can take on complete workflows and deliver real value to customers. We want to bring this everywhere as fast as we can. The second question we hear all the time is: why build another robot? Isn’t hardware and low-level control basically solved? I hear this surprisingly often. I think a lot of the misunderstanding comes from the amazing demo videos we all see online. Robotics can look so impressive that it is easy to feel like hardware already had its “iron man moment,” the body is solved, and now all that is left is the brain. Once you actually deploy robots into the physical world, you learn very quickly that this is not true. We have enormous respect for traditional robotics because we understand how hard anything physical becomes once you care about reliability, precision, lifetime, recovery, maintainability, and cost. Not for a five-minute demo, but day after day in a real operation. Taku was carefully designed and tuned around loco-manipulation, which is still a very underdeveloped domain. A robot that needs to move through the world while manipulating it continuously has very different requirements from locomotion alone or stationary manipulation alone. There are a lot of bitter lessons here that you can only learn by getting into the details. I’m really proud that our team did exactly that and turned those lessons into a real system with the right technologies instead of assuming those problems were already solved. Finally, it’s all about people. No matter how shiny a company looks from the outside, the people inside are what we take the most pride in. The photo below is one of my favorite Dyna photos: 4:30am in the Dyna office, with such a talented and dedicated team happily crafting Taku together. What I love about this photo is not the late hour itself. It’s that the energy, diligence, and hard work come from genuine love for what we are building and pride in the craft. Nobody creates something great just by being told to work harder. The best work comes when people deeply care about the thing they are building, enjoy building it together, and want to make it better. That’s the culture I’m most proud of at Dyna. We dislike bullshit. We just cook and ship. And hopefully, piece by piece, bring humanity forward. For more technical details on Dyna-2.1 and Taku: dyna.co/dyna-2.1

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.

Indeed, I love working with Jason and our entire research team. There was a time when people questioned us: Is this team really capable of doing impressive research? We don’t have professors. We don’t have industry celebrities. We don’t have the familiar names people immediately recognize. I was unhappy hearing that, because I knew how strong our team was. But I stayed quiet. Because I also knew that if we didn’t ship, we couldn’t prove anything. Today, I finally want to say it proudly: we have a world-class research team. Their names might not be familiar to everyone yet, but make no mistake—they are rock stars. I’ve seen firsthand their talent, creativity, rigor, and determination, and I’m incredibly proud of what this team has accomplished. In the end, it’s not about where you come from. It’s about where we’re heading together. Really big thanks to our awesome research team! @JasonMa2020 @kun_h____ @_anhquanpham @chetan_ @georgejygao @TopiwalaAnirudh @johnnywang_16 @YifeiRobotics @tianyurobot At the same time, we are still hiring more world-class researchers for all the directions!!!!!!!!! Please reach out if you want to raise this scaling law to another level!!!!!!! Applied Researcher - Deployment Intelligence & Continuous Learning: jobs.ashbyhq.com/dyna-roboti… Research Engineer/ Scientist: jobs.ashbyhq.com/dyna-roboti… Research Engineer/Scientist, Simulation: jobs.ashbyhq.com/dyna-roboti… Research Internship: jobs.ashbyhq.com/dyna-roboti…

The real impact on the industry: - Data quality still matters, but it is no longer the only thing that matters. True scalability only becomes possible when we can effectively make use of low-precision, imperfect data at scale. - Human video can be transferred directly to the robot embodiment during pretraining, without any retargeting. Real-robot data is no longer the single dominant source of value. In that sense, UMI is essentially an intermediate form factor. - The dynamics in video can genuinely be learned and then transferred to robots through an IDM. The key to scaling is scaling up the learning of video dynamics—not scaling up the IDM itself. - The action model is at the core of System 1, and the ability to follow language is its most important capability.