Lenny's Podcast: Product | Career | Growth
00:06:36 · Lenny Rachitsky
[00:06:36] S1: It's interesting with VR. There's this idea with when you build product, there's always this question when something doesn't work. Is it just you executed it badly or the idea was just a bad idea and it's always hard to know. It feels like with the AR, it's just like so much effort was put into making it work just for a decade, many decades, and just has not worked. So it's nice that we know, okay, there's nothing we can really do right now to make this work. I completely agree with you. The issue is just like, "I don't want to sit on my couch disconnected from the world." And even if I could see people through it's just like, "Eh, I'm just going to ... I don't need this. It's not that big of a deal."
00:07:08 · Caitlin Kalinowski, Lenny Rachitsky
[00:07:08] S0: In AR, you're going to just start getting more and more larger and larger displays. But the great thing about Orion is you've got a 70 degree field of view binocular. So with the prototype, you got to sense what this is really going to be like in the future. It's very hard to describe how it feels to use a pair of glasses like this, but when you do, you suddenly are like, "Oh, I feel immersed. The field of view is wide enough. I feel immersed." And it becomes pretty clear that I think this is part of where the future's headed. It's very odd. Everyone is suddenly asking about hardware and robots and the physical world and it's never been a sexy career. It's always been the thing that you went into because you loved it. It never paid the same as these other careers. It was never kind of at the forefront of how we talk about things with the possible exception of Apple, obviously, and the hardware lineage at Apple. So it's great in some ways and it's very odd in others. [00:09:46] S1: What's surprisingly hard about hardware? A lot of software companies, a lot of people are just like, "Okay, cool. We're going to build some hardware." That's the future, that's the moat now. And they get into it and they're like, "What the heck?" What are some things that maybe people don't think about when they think about, "Okay, we're going to build some hardware." What are some of the surprising challenges that come up?
00:09:46 · Lenny Rachitsky, Caitlin Kalinowski
[00:09:46] S1: What's surprisingly hard about hardware? A lot of software companies, a lot of people are just like, "Okay, cool. We're going to build some hardware." That's the future, that's the moat now. And they get into it and they're like, "What the heck?" What are some things that maybe people don't think about when they think about, "Okay, we're going to build some hardware." What are some of the surprising challenges that come up? [00:10:03] S0: I like to talk to computer science folks about it this way. So computer science folks, as you know, they write code and then they compile the code often and they run the code and debug it, but they can compile their code every day, every hour, whatever they need to do. In hardware, we only get to "compile our code" four or five times. [00:10:28] S1: Four, five times a year or? [00:10:30] S0: Total. [00:10:30] S1: Okay, ever.
00:10:32 · Caitlin Kalinowski
[00:10:32] S0: Right. So if you're building hardware, you redesign it in CAD for every major build and then you have to release it. And once it's released, you compile it the last time you release it for mass production. If it's a mass production device, that's it. You're done. You can't ship over their updates. So we have a different approach. We have to have a different approach, which is more conservative. You have to do more of the reliability checks and tests in line with the program because once you compile that last time, you're done. You make all the parts, you put them together, they're out in the world. The only alternative is to ship something new to replace it a couple years later. And so we have to be more conservative and we have to take our time because if you think about it, a product that sells millions, if you have a graph of all the parts put together on any different part of the device, you have a curve, you're in the plus and minus three sigma or more. So meaning if you have two parts that go together, you're going to get the smallest version of this one and the largest version of this one and you're going to have to put those together across the board. People don't think about this that much, but the part variance is pretty high. And so we've got to solve for that last half a percent in the process of building so that when we compile our last time, when we build our last time, it's done and we're going to have a high yield, we're going to be able to make them and make money on them effectively and we won't have very many returns. And so that's kind of the game that we're playing.
00:12:08 · Lenny Rachitsky, Caitlin Kalinowski
[00:12:08] S1: It sounds so hard and complicated. They're just like, "Software is so nice, you just write some code, ship it. It's great." Why do you think people are getting so into robots and hardware now? What's the driving trend? [00:12:20] S0: Yeah. What I'm seeing in the AI world in San Francisco is there's a dawning realization, especially in the labs, I think that the acceleration is going so vertical that what you can do behind a keyboard with AI is going to saturate. And I don't know when it's going to saturate and nobody else knows either, but when that happens, the next frontier is the physical world. And so what I see happening is the labs, big tech, startups are all realizing at the same time, "Okay, this is coming. We're going to have complex systems that can solve problems in the digital world very, very quickly. We already have them. They're going to get better and more comprehensive and more capable." If you think about that as a frontier, you could see the end of that tunnel. Now, I don't know when it's going to be again, but we can see that that's going to saturate at some point, or at least people think it will. And when that happens, the next frontier is hardware. The next frontier is robotics, manufacturing, industrialization, the sensing layer in the real world, the ability to move objects in the real world and eventually we hope space.
00:13:34 · Lenny Rachitsky, Caitlin Kalinowski
[00:13:34] S1: So one of the most interesting lines of development is these humanoid robots, that's kind of like our meat brains are always more attracted to robots that look like us and act like us. There's a few companies very ahead. There's Optimus, Tesla, there's Figure, there's NEO, there's a few others. What's your sense on just the current state of these humanoids and kind of where ... I don't know, how close are we to humanoids being around us? [00:13:58] S0: We might be close. I have, like many others, safety concerns about large, strong humanoids operating right next to people because we have to have enough data to show that that's safe. There are some designs and 1X NEO is a good example of this that have made significant safety considerations in their designs and pulled mass inwards essentially, which is a lot safer. Softer robots is safer. [00:14:25] S1: Just to clarify, you're saying they're lighter and so the impact of a robot hitting you is less.