Lenny's Podcast: Product | Career | Growth
00:04:55 · Lenny Rachitsky
[00:04:55] S1: That is interesting. So just like the investment and innovation that happened that went into VR has actually proven to be really useful. And so it feels like the companies that have put a lot of effort into that and money into that are ahead on the next step. So is that where you think things go? Where do you think things are going? Is it AR glasses? Is something else? What's the future of this?
00:05:15 · Caitlin Kalinowski
[00:05:15] S0: I believe in AR glasses as part of the future because I do think looking down at your phone all the time is not great for us as social creatures. So if you can maintain social connections and get information, that's where I think we're headed. Orion, the AR glasses we worked on, I worked on most recently are a bit ahead of their time because they're using waveguides and MicroLED that are not quite ready for mass production. The yields just aren't there. The cost is still high. I think that's absolutely a path that AR glasses are likely to take. And as we figure out the input to those glasses, how do you communicate with them when you're on the move, when you're in public? How do you communicate quietly, silently with them? I think once we start to figure out some of those challenges that having a display that's mostly off that you can turn on when you want it to be on seems like part of the future. So that's part of it. The other part is there's this lineage of technology going through VR and then AR and now in, I'm using the term robotics, physical AI, but you really have to step back and look at autonomous vehicles, drones, obviously robots, autonomy period, manufacturing. All of these technologies are going to need the same piece parts, the same pieces that we built in the AR/VR spectrum.
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.