00:51:46 · Eric Seufert
[00:51:46] S2: it just doesn't apply anymore. Mhmm. Right? He was talking he had this idea of the dependence effect. Advertising actually is a way to whip up demand so we can maximize production because that was what he called this conventional wisdom at the time. You should be maximizing production. Mhmm. But the the reality is in the time he wrote this book, 1958, you had people moving to the suburbs, getting big houses. They you know, the GI bill helped them buy these homes. You had the suburb the the idea of the suburbs was was being deployed. And so, you know, people needed washing machines. They needed cars. They needed refrigerators for the first time. And so you had these big companies that made these mass market goods. They advertised in mass market media. Mhmm. And they and and and John Kenneth Galbraith's idea was, like, well, that's creating demand. The the there there is no actual inherent demand for these things. It's creating it through advertising. And my point is the opposite. Yep. You know, we don't have this homogenized society anymore, and we don't have people that need these homogenized goods anymore, and we have a lot more particular specific media now. And we can reach people and advertise to them the things that they have demand for, for products that weren't economically viable prior to these systems, these distribution systems. And that is the Prosper Society, is being able to reach people to meet the demands that they have with the products that they couldn't access before with ads that they wouldn't otherwise see absent these systems. And so I think it's it's, you know, I think it's a very it's my sense is like you can make a very credible bull case that that's what AI delivers to us. And it's not about wiping out white collar labor because the reality is, like, that's gonna create more jobs. And we're seeing that now. Like, there's no there's no justification for that skepticism. It just doesn't exist. We're seeing an increase in hiring. Maybe not at the entry level, and you could Yeah. Discuss if there should be some intervention there. But my sense is, like, AI actually if you look at the data and there's a Financial Times article about Standard Data. If you look at the data, it doesn't support that bear case. And so Yeah. That bear case should be absolutely eliminated as something that even enters the conversation.
00:53:37 · John Coogan
[00:53:37] S1: Yeah. No. I completely agree. That was an amazing speech. I don't know if I have any real yeah. No. I I I love this idea. We've been talking about a bunch just, more customization, the long tail of commerce getting even longer, and it feels unfathomable. We've seen this with, like, you know, specific shirts that are just for you designed and targeted to you on Facebook. We've seen that, but, like, it can, in fact, get more personalized.
00:54:01 · Jordi Hays, John Coogan
[00:54:01] S0: Well, yeah, I mean, we've we've seen this with, like, content Yeah. These platforms. Right? They're very good at they're very good at, like, find they're they they they are very good at serving you. They can serve you a video that has 50 views from a new channel on YouTube, and you'll be like, that's interesting. I will watch this. Right? And it and then and it's a niche that is, like, so small. It never could have existed in the era of, you know, radio and television and, you know, seeing that seeing that trend accelerate. It reminds me, I was I I was wanting, you know, you know these, like, kids RC, like, ride on cars? I got, like, incredibly frustrated with these because I've tried a bunch of the different brands. I've tried spending, like, $800 on them and, you know, $400. And all of them just suck, like, the kids, even when you have the you're driving the kid on the controller, the kid can still hit the gas and just like run into stuff and, you know, it's just absolute chaos. I'm like, what is the version of this that is like, you know, you know, the the top of the line version because of I wanna get it. I I use these things a lot. And I searched around, couldn't find it anywhere. And then John just, like [00:55:05] S1: Within twenty four hours, I got served exactly what he was looking for. I don't even know how how it got served to me, but it was fantastic. Thank you so much for taking the time to come chat with us. [00:55:15] S0: Electric. [00:55:15] S1: Back. Always always a great time.
00:55:15 · John Coogan, Jordi Hays, Eric Seufert, Sean Frank, Bernt Øivind Børnich
[00:55:15] S1: Back. Always always a great time. [00:55:17] S0: My favorite my favorite conversations. When you're on, it makes me feel like we're on we're on we're actually, you know, on SportsCenter. Yeah. Because most people that have talked about this stuff are not high energy. It's just like full on sports center. It's amazing. Amazing. I love [00:55:31] S2: it. [00:55:31] S1: Well, congratulations on all the progress and and the Prosperous Society. Go listen to it. It's a three hour four part series and sign up for Mobile Dev Memo if you haven't already. Of course, you should. But thank you so much for coming on the show, Eric. We'll talk to you soon. [00:55:44] S2: Take care, guys. [00:55:45] S1: Have a good one. Let me tell you about public.com. Investing for those that take it seriously. They got stocks, options, bonds, crypto, treasuries, and more with great customer service. And our next guest is Bert from one x. He's the founder and CEO. How are you doing? What is that? [00:55:59] S6: What is you. [00:56:00] S1: Welcome back to the show. Thank you so much. That is incredible. [00:56:04] S5: My hand, and then it's like Neo's hand. [00:56:06] S1: Yes. Introduce, the launch today. What happened? Tell us about it.
00:56:06 · John Coogan, Bernt Øivind Børnich
[00:56:06] S1: Yes. Introduce, the launch today. What happened? Tell us about it. [00:56:10] S5: I mean, we've been cooking on this for quite a while, so super excited to finally show this to the world. Also, it's just so exciting because we're so close to shipping now. Yeah. So everyone's going to pick this apart anyway. Yeah. So we don't need to be careful anymore and we can just open it up. To me, it's this beautiful machine that becomes almost more art than engineering, right, at this point. Yeah. We're very excited to show people what we've been cooking and excited to put it in people's hands. I think also, has it a special place in my heart because we're working on this problem for more than a decade. And one thing that Neo and One X has really been pushing is how to use highly miniaturized, high power motors and tendons to create these machines that mimic humans. The hands is the kind of culmination of all that work, right? It's where all of the complexity comes together to meet the world, hopefully we've created something here that can really remove that final barrier for how intelligent our models can become. Right? Like Mhmm. So much of human intelligence comes from our ability to to probe the world for truth and to really figure out how the world works through our hands.
00:57:21 · Jordi Hays, Bernt Øivind Børnich, John Coogan
[00:57:21] S0: Okay. Take us through the full journey of developing hands for Neo. Lot of people in tech love to, you know, follow an an Elon style playbook, just make the most simple version of something, simplify, simplify, simplify. This looks incredibly beautiful, but also incredibly complex. And so I wanna understand, like, how you how you got here basically, like, version by version. [00:57:48] S5: So it is a very complicated hand, but in my opinion, it is the simplest version of it that exists that is good enough to do what needs to happen. So first of all, Onex is fully vertically integrated, so we do absolutely everything in house and in our factory here in California where I'm sitting right now. We do everything from designing the production processes to producing our own motors, our own tendons, the entire system, right, sensors, electronics, everything in house. And that allows us to iterate very, very fast Because I do really believe in, like, you have to have this first principles approach. Right? So start with, like, what what am I trying to solve? We're quite lucky in that we have, humans to look at with respect to how do you solve this problem. Nature did a pretty good job. [00:58:33] S1: So when you say you're you're you're looking at humans, what type of training data are you using? What's useful? What are you discarding from just there's a lot of hand videos on the Internet, I'm sure. People doing all sorts of things. You can do teleoperation. You can have people wear gloves and do motion capture. You can do simulation. [00:58:51] S0: X-ray.