Lenny's Podcast: Product | Career | Growth
00:49:32 · Lenny Rachitsky, Caitlin Kalinowski
[00:49:32] S1: So let's say Matic is an example. How many components are there that they all have to assemble and not have one not available? [00:49:38] S0: I'm doing the math in my head. They probably have between 50 and 150 parts. It's possible that they have more. I haven't seen their CAD, so I don't know what it's like inside their device, but they do have a lot of things going on. They have the wheels of the device that are obviously moving around. Then they have a vacuum, but they also have a mop, and obviously they have a vacuum bag. They have the reservoir that- They have a vacuum bag. They have the reservoir that the liquid has to go in for the mop. They have a system which I think is SLAM-based, which can see your room and make a map of it and identify which surface is which, and that, I believe, stays on the device, so it doesn't go up to the cloud, which is also what we did in VR as well, which I think is a good privacy practice. And then, they, of course, have wireless modules that connect up so you can communicate with your device. They're going to have a SoC silicon, they're going to have RAM, they're going to have PCBs. If you take everything off of those things like all the little caps off the PCBs and everything, then you're in the thousands of parts easily. So it depends on how you count, but this is not a simple device. [00:50:48] S1: And all it takes is one piece to not be available.
00:50:48 · Lenny Rachitsky, Caitlin Kalinowski
[00:50:48] S1: And all it takes is one piece to not be available. [00:50:51] S0: Yeah. So imagine you're a vendor that sells you a component that's a die-cast component, goes out of business. You can get another die-cast component in three months maybe and at quantity in five months or something like that at high quantity. This is recoverable. If your silicon goes out, if you can't buy your silicon, you can't buy your chip. Now, you have to redesign your board, and you have to find something else that might work. This is a catastrophic redesign. If you can't get the RAM you wanted and the form factory you wanted, this is what I call a... Essentially, it's a catastrophic redesign. You now have to redesign the entire guts of your product and then secure supply chain for these new things, build it again on the production line, test it again, do all the reliability testing. It is non-trivial, and so this is why we care. So there's a hierarchy of components. Often in consumer electronics, we start with silicon and the display, which are the longest lead time things usually in my world. In robots, actuators are pretty tricky to get, even just for prototyping. Sometimes it takes a month or two to buy an actuator. [00:52:01] S1: This is why Elon famously just starts building it all himself.
00:52:01 · Lenny Rachitsky, Caitlin Kalinowski
[00:52:01] S1: This is why Elon famously just starts building it all himself. [00:52:04] S0: Well, when you look at what he did with Tesla and verticalizing his supply chain, and famously, actually, Starlink is an even better example of this where I believe it's effectively like or and silicon chips in, product out. That's a pretty incredible factory I've heard. I'd love to see it someday. But this is where verticalization comes into play because if you have verticalized and you have a lot of the components in-house or you're building a lot of things in-house, you can actually adapt to supply chain shocks better and then famously he did. When the silicon itself was difficult to find, he was able to redesign his PCB in record time and adapt to buying new silicon. And that would be much more catastrophic for a company that had a more classic supply chain. [00:52:53] S1: One of the big decisions that I imagine you have to make when you're designing a new piece of hardware is deciding between using this available stuff, available components that are out there cheap versus, "Okay, we're going to do this something new." It's something in software too, to use the design system or do something new. How do you think about that balance when you're designing a new piece of hardware?
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.