Sources with Alex Heath
00:27:10 · Ellis Hamburger, Tony Zhao, Alex Heath
[00:27:10] S1: Well, does Memo have a point of view on whether you should rinse before loading or load with the food on? Because I know there's controversy around that as well with the Cascade Platinum Reddit crew. [00:27:21] S0: To to add context here, there's this whole, I think, whole debate, right, of, like, the dishwashers will be able to clean your dishes better if you don't rinse as much. There's a whole, like, theory around it. But I think for us, like, when we teach a memo, I think it will rinse. We we do not have, like, big chunk of food in the... Inside the dishwasher, which I think is is more more... There's more downside than the upside it can give. [00:27:49] S2: Yeah. Well, you know that's big dishwasher that tells you that you need to rinse. That's that's the lobbying operation. [00:27:57] S1: That's Bosch and co. For those who missed the last episode, Bosch's tagline from CES. The more you Bosch, the more you feel like a Bosch. That's not a joke. That was their actual tagline from the last time around. Well, I know like a lot of the models for robots and otherwise, we also talked to the CEO of Rivian recently. They talk about how they don't actually program rules of any kind. They just gather more data. Is that similar with you guys as well with the Glove? Absolutely.
00:27:57 · Ellis Hamburger, Tony Zhao, Alex Heath
[00:27:57] S1: That's Bosch and co. For those who missed the last episode, Bosch's tagline from CES. The more you Bosch, the more you feel like a Bosch. That's not a joke. That was their actual tagline from the last time around. Well, I know like a lot of the models for robots and otherwise, we also talked to the CEO of Rivian recently. They talk about how they don't actually program rules of any kind. They just gather more data. Is that similar with you guys as well with the Glove? Absolutely. [00:28:31] S0: Yeah. It's just that I think if you want robots in the real world, in the open world, like you cannot, if else, all the things that can happen. And trying to engineer that is kind of... You'll be kinda cursed if you try to enumerate all the things that can go wrong. But the nice thing about data is that all of these if and else are somewhat included inside the data. So if we have... Get very diverse high quality data and enough quantity of them, we don't actually need to do much like real coding, but the neural network will learn from data and and that ends up being better. [00:29:10] S1: As someone who's very close to this stuff, are you more or less optimistic than the average person about riding in a Waymo or Tesla self driving? [00:29:20] S0: I own Tesla. I self drive to to office every day. [00:29:26] S2: You used to work at Tesla. Right?
00:29:26 · Alex Heath, Tony Zhao
[00:29:26] S2: You used to work at Tesla. Right? [00:29:28] S0: Yes. I I did. I spent a summer there back in 2022. That was actually before the whole, like, end to end, scheme happening there, back when things are not working so well. Like, these days, work very well. I feel like I get more optimistic when... Of, like, going... When seeing things that doesn't work and believing that it will go to, like, 99. But going from that to robotaxi that is fully, fully autonomous and can scalably be deployed in any place, I think there... People might have, like, underestimated how much work there. So I think I'm more optimistic for always the the first chunk of it, and the second lag is, like, more challenging. [00:30:13] S2: If we could get a little more technical for a second... And and robotics are in the brain for me, Tony, because today, I interviewed Demis Hassabis. He used to work at DeepMind. Right? Yep. So... Yeah. So Demis and then, the CEO of Skild AI, which just raised a big round and is doing robotics in the enterprise. And everyone is approaching it differently, but Demis and and Pathak from Skilled were saying, like, they think enterprise is the right way to attack this problem from a data perspective. And it's interesting to see you do it from the home, you know, going towards the consumer route. And I'm curious, like, how you landed on that as the way that you all were gonna go versus just doing the more kind of boring but stable, safe enterprise route.
00:31:02 · Tony Zhao
[00:31:02] S0: Yeah. So I I think this is maybe a hot take, but I think there is a myth around, like, industrial automation or, like, in factories, it's an easier, like, thing for the robot to do. And that might be intuitive for people. Right? There's more constraints. Like, there's clearly well defined things that you should do. But I think the broader context here is more like if you want to do robotics, like, pretty much everything is hard. Like, a 100% of the things will be hard. And putting in that context, I think the relative... Like, the hardness difference between going for the industrial automation or going for, like, homes, I think it's small in that perspective. And then I think the question becomes like, hey. Are we really asking the right question of, like, trying to find things that are easy? Because everything is hard. And what we end up with there is, like, I think we would just want to work on something that is really inspiring and exciting to people, and that will help us build the best team. And that will help us gather people who are, like, truly excited about the mission and, like, putting the actual work, putting, like, all the ingenuities into making that a reality. So I think that's the... Maybe the hot take here is in a grand scheme of things, I think being, like, 20%, 30% harder or easier really doesn't matter that much. And I think there is also a a a different viewpoint here when it comes to home versus, like like, say, factories. Right? One of the big thesis we have is we believe robots will win on the availability before they win on the sheer, like, productivity or, like, the speed. So if you think about, like, factory automations, you're essentially competing with hourly wage workers and essentially humans. And human hands are so perfect after, like, millions years of revolution, and we're so intelligent. And it's actually really hard to beat the throughput and the precision that human has. But these are absolutely critical in factories because if you're slow, like, the whole line will be blocked by you. And if your success rate is not high enough, maybe you'll be damaging some parts, and that's, like, causing real economic damages.
00:33:29 · Ellis Hamburger, Tony Zhao
[00:33:29] S1: Right? [00:33:29] S0: So if we think about it in in that context, home actually becomes quite easy because the type of work that we need to do is not like rocket science. It's not like assembling an iPhone. It's essentially things that a six year old knows how to do, And it's one of the few places that you can provide a lot of value by, like, maybe giving people a cup of water when they're tired and maybe refilling that water, like, time over time, making sure it's always, like, the perfect temperature and perfect volume there. The chores are they come, like, every day. Every day, there is new, like, 30 to an hour of chores getting invented in your homes. But you cannot just have a housekeeper to come in for, like, thirty minute and you let them go. Right? You either are, like, super rich and you have someone in your home the whole time. They're your, like, housekeeper. You pay, like, a full time salary for that, or you actually need to do it yourself. And so the time we save becomes not like competing with a hourly wage worker, but becomes your opportunity cost of, like, hey. If every day after work, you have, like, three hours you can spend, and if you can save one more hour a day, it's actually quite significant. So it becomes, like, almost like a labor arbitrage that if you're making more, the robot will be more useful to you earlier. And that allows us to kind of get into the markets, like, gradually as opposed to, like, we're spot on competing with human dexterity, like, from day one.
00:35:00 · Ellis Hamburger, Tony Zhao, Alex Heath
[00:35:00] S1: I get so mad at people who have three hours after work every day because clearly they don't have kids or they're super rich as you said, and they have their own assistant or butler or something. I was like, I have approximately like forty three minutes of free time per day. And if I have some music on, doing the dishes or whatever is not the worst thing in the world, but when that is very literally the time that I have to like read a book or play a game or spend the time with my wife, it's like, that is very valuable time. And so I do I do see some some need there. And I know the rumor is that Apple's working on something like this as well. Right? [00:35:41] S0: Yeah. I think pretty much everyone is. If you look at the latest demos coming from Tesla, from Figure, it's all about the homes. Right? [00:35:51] S2: Do you think we actually have robots in the homes of, like, everyday normal people, though, in the next even five years? I don't know, like, when you... What you have is so well thought out. It's like the videos, the branding, the positioning, it really resonates. It's very intentional. Shout out Ellis, I'm sure, for a lot of that too. But when do you think this actually shows up for people in a way that's, like, cost conscious, that's accessible, that's scalable?