Sunday Robotics: Scaling the Home Robot Revolution with Co-Founders Tony Zhao and Cheng Chi

Nobody wants to do their dishes. Nobody wants to do their laundry. People will love to spend more time with their family, with their loved ones. So what we believe in is that if the robot is cheap, safe, and capable, everyone will want our robot. And we see a future where we have more than 1,000,000,000 of these robots in people's homes within a decade. Thanks, Memo. Hi, listeners. Welcome back to No Priors. Today, we're here with Tony Zhao and Cheng Chi, cofounders of Sunday, makers of Memo, the first general home robot. We'll talk about AI and robotics, data collection, building a full stack robotics company, and a world beyond toil. Welcome. Chang, Tony, thanks for being here. Thanks for having us. Yeah. Okay. First, I wanna ask, like, why are we here? Because classical robotics has not been an area of great optimism over time or, like, massive velocity of work. And now people are talking about, a foundation model for robotics or a Chad GPT moment. Can you just contextualize, like, the state of AI robotics and why we should be excited? I will say, I think we're kind of in between the GPT moment and the CHAD GPT moment. Like, in the context of LMs, what it means is that it seems like we have a recipe that can be scaled, but we haven't scaled it up yet. And we haven't scaled it up so much so that we can have a great consumer product out of it. So this is what I mean, like, GBT, which is like a technology, and Charge GBT, which is a product. Yeah. And so we're seeing across academia, there's consensus around what's the method, for manipulation, but everybody's talking about scaling up. It's like, we know there's sign of life for the algorithms people are picking, but people don't know if we have more data like what happened to g p t two, g p three will happen. And but we see a clear trend that, you know, there's no reason to believe that robotic doesn't follow the trajectory of other AI fields that, you know, scaling up is gonna improve performance. Maybe even if you took a step back, like, what was the process for deploying a robot into the world, like, ten years ago? Like, pre set of generalizable AI algorithms? Like, why why was it so slow as a field? Yeah. So previously, you know, classical robotics have this sense plan act modular approach where there's a human design and interface between each of the modules, and those are need to be designed for each specific task and each specific environment. In academia, that means for every task, that means a paper. So a paper is you design a task, design environment, and you design interfaces, and then you produce engineering work for that specific task. But once you move on to the next task, you throw away all your code, all your work, …