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Latent Space · 4 min · Oct 13, 2026

Multi-site rollouts, not pilots, prove a robot creates value

Jason Ma

Co-founder & Chief Scientist, Dyna Robotics

First author of Eureka, in which GPT-4 wrote robot reward functions that beat expert ones; previously at Google DeepMind and NVIDIA.

  • Shawn Wang (swyx)Host · Co-founder, AI Engineer

    Wrote “The Rise of the AI Engineer” and founded Smol AI, maker of AI News.

About this segment

Jason Ma describes Dyna robots folding thousands of napkins daily across Din Tai Fung locations, which led to a larger contract. He argues pilots are easy; customers expanding to many sites is the real test.

From “Robots That Predict the Future: Dyna-2 & Physical AI — Jason Ma, Dyna Robotics”, Oct 13, 2026. Watch the full episode

“What's really hard is that the customers believe or validate that your robot creates enough value that they want to deploy your robots across many of their sites.”
Jason Maat 39:37 · Latent Space39:37

Takeaways

  1. Din Tai Fung expanded Dyna from napkin folding to food cutting after scaling
  2. Deformable objects defeat specialized machines; general models adapt faster
  3. Shared weights give consistent quality across locations via OTA updates

In this episode

Full episode
  1. 38:50Multi-site rollouts, not pilots, prove a robot creates value4:05

  2. 56:32Why predicting raw video beats VLAs, and where that bet could fail4:16Up next
  3. 10:12Robot data quality changes as the model improves3:36

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

  1. 38:53

    Yeah, so if you want to go somewhere, I actually recommend going to our other deployment, Dintai Phong. Have you guys been to it? Yes. Yes. yeah you know the best very thin wrappers yeah very we should have filmed there yeah no we should have just done there a b-roll yeah but uh in din tai phone like if you go to din tai phone or other like uh you know somewhat upscale restaurant there's napkins on your table right so our robots our robots today fold thousands of napkins for many many different locations many different din tai phone locations in the united states so that's actually our biggest scaled customer So I want to make a distinction here.

  2. 39:32

    Today, there's a lot of robotics companies that's talking about deployments, talking about pilots. But what's really hard is not putting your robot in one location or doing a pilot. What's really hard is that the customers believe or validate that your robot creates enough value that they want to deploy your robots across many of their sites. So that's what's happened for us with Jintai Phone, which to the best of my knowledge, hasn't really happened for other companies doing similar like general purpose robots so at dintai phone our robots fold thousands of napkins per day it's the quality standard is extremely high you know because they want like okay uh the second forward just seam of the napkin is only like a couple centimeters below the uh i don't even know how you call it the pocket so you can stick the chopsticks in so it's actually very

  3. 40:20

    fine grain high precision has i can show you at some level that's what robotics are good for robots are good for too right um humans are not the most consistent yes especially in small details whereas a robot can somewhat do it perfectly it's interesting that's the example you bring for general purpose robotics versus uh at some level i think people would assume yeah you can have a specialized robot that will fold napkins perfectly to the exact you know millimeter as opposed to something like this but yeah i'm sure uh maybe you can't right but the hard thing about manipulating deformable object, right?

  4. 40:56

    Like napkin, towels, everything is deformable. There's just infinitely many possible configuration. So that's really hard for a specialized robot, right? And what we find is that by developing a general purpose model on a robot and adapting it to these use cases, you can actually get to your production quality faster. And what's really nice is that now that Dintai Phone has scaled, has signed a much bigger contract with us, they not only want our robots to fold napkins, now they want our robots you know like cut you know that's why we had the i mean example to cut food to the other is that actually the second use case and that's one of the use cases i think oh i mean that phone needs to cut so many vegetables per day they need to do the dumplings exactly to the fold that's the final boss that's the final uh i think you can i think you have the dexterity

  5. 41:41

    and precision to do this yeah you have a little pobo but like you know don't break the dumplings yeah yeah i mean that's that's the obvious benefit is you can adapt them anymore i think the other thing you really touch on is the speed right yeah to make a custom napkin folding with the exact ply whatever yeah you have to make the machine you can't just amazon this yeah but this can do it pretty fast yeah this can do it very fast and now it's actually oh it's a human parody yeah you know and this robot doesn't get tired doesn't need to uh you know you can and i think i really like the fact that you mentioned consistency right like one issue with din taifan is like you know they're LA location with New York location, Seattle location, they fold napkin slightly differently.

  6. 42:27

    But from their perspective, they want consistency. But, you know, that's exactly what these models are good at. Because models are, you know, just it's a set of weights. You can just like put the weights in the different robots across. I mean, I think you can OTA update them as well. Yeah, you can just update all of them at once. You know, it just, once it works, I think it's truly like, yeah, incredible.

  7. 42:48

    final boss is cheesecake factory if you know the story there they're from scratch everything is