Lenny's Podcast: Product | Career | Growth
00:53:12 · Caitlin Kalinowski
[00:53:12] S0: Very simply, I use off the shelf whenever I can, especially in the prototyping phases because in the prototyping phases, which are really important phase of what we do, your goal is to show that it can work at all. Can you get a thing working? So often, it doesn't have to be the final pretty thing. It can be the ugly version. You can make an industrial design model of what the final thing's going to look like, but actually, we call it works like looks like models where you have, "This is what it's going to look like and here's how it's going to work and here's a working prototype." Humans are pretty good at this as long as, and this is a pretty big caveat, what you show could fit into the industrial design. For companies that are younger, that's not always the case, but that's what we're going for. And so, in the prototyping phase, man, whatever works, off the shelf, whatever's fast, whatever you can get to quickly, and then maintain a sense of what's really going to fit in your final design. Is it capable? Are the processes and components and materials capable of actually adapting to this size, this new weight that you need it to go into? So that's part of the calculus. When you move into mass production and the final design, it depends. I mean, if I was making Matic and I could buy an off-the-shelf wheel or an off- the-shelf component, I absolutely would and fit it in. But often, what we're doing is highly custom because we have, again, one of those KPIs, "I want to be this size. I want it to be this weight. I want to be this color." And often, off-the-shelf parts aren't good enough, not because they don't work, but because they're just not exactly designed for what we're doing.
00:54:50 · Lenny Rachitsky, Caitlin Kalinowski
[00:54:50] S1: This is the reason these drones are so cheap now is there's all these parts that have been innovated and built and scaled, manufactured for other things. And now, we just have all these things, and we can assemble a really cheap drone. [00:55:00] S0: Yeah. Yeah, exactly. [00:55:01] S1: Super. You've mentioned CAD a bunch of times and it makes me think about just like CAD has been around for a long time. Just like is AI impacting the way hardware is built? Obviously, it's impacting the way software is built in a huge way. Has it changed your life and the lives of people building hardware and robots?
00:55:17 · Caitlin Kalinowski
[00:55:17] S0: Yeah. So I want to zoom out a little bit. So most of the hardware work goes into prototyping, into 3D CAD, so designing 3D parts and assemblies and components and making sure they work together properly, then in making sure those parts and components can be made by a vendor at quantity that is possible and the tolerances we want, and then putting those things together. So that's kind of our process. Right now, we're right at the very, very beginning of AI being able to do CAD. So I'll give you an example. Claude can do what is essentially surfaces or point clouds. This is not real CAD. Real CAD is, in my world, is dense. It has shape. It has nerves. You have an equation for how the surfaces work, and it's an entity that's designed in CAD. It's a solid entity. And so, right now, we're not quite there with AI doing CAD. I think it's likely that at some point, we will be there. This will be probably one of the biggest changes for my field that we have is being able to, I hope, do rapid design and increase the speed. Now, there's a lot of really fun things to do in CAD, but like in the beginning of my career, we had to do custom screws, and we had to do the 2D drawings for everything. There's a lot of things in CAD that are not as fun. Tolerance stacks, we need them. How does seven parts fit together and are they always going to fit together properly? But it's not fun. It's not the most fun part, maybe for some of us, but not for me. And so, being able to do these things in AI would be amazing so you can focus on actually doing the fun stuff. Another good thing is PCB. A printed circuit board has a lot of layers in the inside and then components that go on the top. If you've ever opened anything like a calculator or a computer and looked inside, you know what I'm talking about, these printed circuit boards. It's increasingly looking like AI can route inside of these boards pretty well, and it's looking like AI is going to be able to do some basic component selection and layout on these boards. So that's kind of where we're at right now. So we're not in a point, Lenny, where day-to-day mechanical or electrical engineering, like the meat and potatoes of it is being done by AI, but there's a huge amount that you can do as an engineer using AI in your strategy, your planning, your ability to think through the complex dependencies that you're facing.
00:57:41 · Caitlin Kalinowski, Lenny Rachitsky
And that's what I use it for now, which is really high-level planning, asking for information. When I look at who else is making a product like this, I use AI to build the databases, and they're not perfect. Certainly, a lot of times something's wrong, but it is so much faster. AI is pretty good in Excel right now, and of course, Excel is one of our favorite tools in engineering. So the ability to actually rapidly make Excel spreadsheets and change them is... It doesn't sound sexy, but actually really speeds up the design process outside of these core pieces. [00:58:16] S1: I love how Excel is always at the bottom of everyone. Anything, no matter what we're doing, we're going to Mars, there's an Excel spreadsheet that's probably driving a lot of this. [00:58:23] S0: Probably. [00:58:24] S1: So what's interesting about what you shared is it has already impacted the work of building hardware and robots, but it's like on the verge of being transformative if it can get to real CAD.
00:58:24 · Lenny Rachitsky, Caitlin Kalinowski
[00:58:24] S1: So what's interesting about what you shared is it has already impacted the work of building hardware and robots, but it's like on the verge of being transformative if it can get to real CAD. [00:58:33] S0: Yeah. And my big question is what is it going to take? So a lot of AI is based on LLMs, which are essentially word generators, word guessers. They're more complicated than that, but that's essentially what they're doing. There's also video models that you've seen that are trained on video, but these models don't understand... They're not very good for what I need, which is I need to know, "Hey, you take a piece of paper, you fold it four times, and you do this. Where's the hole going to be when you open it back up?" These LLMs and even video models, they don't know how to do that. They don't have the ability to understand friction or weight or contact, pressure, friction, surface texture. They're just not able to do these things, and this is the core of what we need in engineering to be able to understand to build things. So some world models may actually be able to do this in the future, and I suspect we may need those models to be the base of CAD and other physical engineering work. And so, my frustration, and this is like a healthy frustration, is I want Codex for engineering. I want Codex for hardware engineering, and it's extremely valuable. I've used it a lot for other things, but I want it for my field. And so, what I think it may require is new model types.
00:59:55 · Lenny Rachitsky, Caitlin Kalinowski
[00:59:55] S1: Sounds like an opportunity to me. I know there's a bunch of World Lab companies. Fei-Fei was on the podcast with World Labs. I think it's called World Labs. Yeah. And then, I know Google's building Gemini. So do you feel like those are the right directions or it's just like, now, we need something actually different? [01:00:10] S0: I don't actually know what the latest of what Fei-Fei is working on, but obviously, she's a brilliant roboticist, and I'd love to learn more about what she's doing. So I'll have to look that up. What I've seen is that what we have right now and what models we're building are going to be part of the solution, but not all of it. [01:00:27] S1: Coming back to robots and humanoids, something that we were chatting about this earlier, your sense is humanoid robots aren't necessarily the answer to a lot of the problems that we have and opportunities that exist. Talk about just your sense of humanoids versus non-humanoid robots.