Lenny's Podcast: Product | Career | Growth
00:47:41 · Lenny Rachitsky, Caitlin Kalinowski
[00:47:41] S1: That's a number I saw, let's not quote that. And you're saying, yeah, from what I hear, it's AI-driven, just like you need ... And when you talk about memory, it's like DRAM and things. What is memory, when we talk about memory? What's going on there? [00:47:55] S0: Processing. So the way to think about it is processing memory. You think about memory on your hard drive or your solid state drive, where you're keeping files that you're not using, essentially, in many cases or that you're dealing with maybe documents or pictures that you have. Maybe that's in cold storage on a server, maybe that's using a hard drive somewhere. This is usually things that you don't need really, really fast access on. But if you're running a program, some of that program is actually going to be run in RAM. And so there's different kinds, obviously, for servers. There's different kinds of server racks. Some server racks are actually focused on this type of memory, and some server racks are focused more on what we consider like a cold storage or a slower ... Now, this isn't my area of expertise, but certainly most of the products that I built, maybe all of them have had RAM, and we've had to figure out how to ... For me, mostly it's a packaging issue. Where do you put it? Does it need to be accessible? Which RAM do you pick? How fast does it need to be, and what is the cost? Is usually our trade-offs. [00:49:10] S1: And what is the bottleneck with more RAM? Is it just the companies that make memory are just not able to produce at this rate, because there's so much demand?
00:49:10 · Lenny Rachitsky, Caitlin Kalinowski
[00:49:10] S1: And what is the bottleneck with more RAM? Is it just the companies that make memory are just not able to produce at this rate, because there's so much demand? [00:49:17] S0: That's right. That's exactly what's happened. [00:49:19] S1: So this is a really good specific example of just how hard it is to build hardware. All it takes is one piece to be not available, and your whole thing's screwed. [00:49:29] S0: Yeah, you can't build anything if you have one component missing. [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?
00:49:32 · 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.