Lenny's Podcast: Product | Career | Growth
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.
01:00:42 · Caitlin Kalinowski
[01:00:42] S0: Yeah. I think there's a hype cycle around humanoids. That doesn't mean they're not extremely interesting, and I think there's going to be winners there. But what I hear a lot is, "I want a generalist robot shape to do everything." I don't know that that works. I think that you need different types of robots to do different types of things. For example, if you've got a laptop and if you want to screw together the keyboard to the case, this is not a job I don't think, for a humanoid. This is a job for a dedicated robot, manufacturing robot that has been designed just to screw 10 screws into a case for this specific laptop and you want to do that 10,000 times a day or something or 10,000 times a week or something. That's a dedicated robot that's specifically intended to do that thing. What I think is interesting here is you can have standard cabinet sizes for automation robots and you can have them be modifiable over time, and that's going to be a very interesting field, I think, is how do you make manufacturing robots that are adaptable and changeable, but you wouldn't want a humanoid to do that. And so, when you really go and look at a modern manufacturing facility like in China, at the top tier of tier one suppliers, there's not very many people on the line anyway. The entire printed circuit board line is essentially got no people on it anymore. The raw board is going through and getting reflowed and getting checked, and the whole thing is being done without humans unless there's something goes wrong and a human runs over and fixes something. So in mechanical assembly, the same thing. These most advanced lines, they don't have people working very much. They used to have 200 people. They might have 10 now. And so, we've already kind of moved past human labor in a lot of this most advanced manufacturing. And so, we don't actually need to replace humans with humanoids. We just need more of these dedicated robots. So my suspicion is we'll have humanoids for some long-tail things that we need to do that humans are currently doing that will be important, but we'll also have robots that are for construction, robots that are for electrical work, robots that are for very low volume assembly maybe, robots for logistics, and most of them are going to look different from each other.
01:03:05 · Lenny Rachitsky
[01:03:05] S1: That makes all the sense in the world. What I think about as you talk about this is it feels like there's going to be this big moment when a robot can build other robots, and this CAD point you make about where once AI can develop full designs for hardware, that's going to be a big moment. Do you have a sense of just how close we are to this, I don't know, this loop that begins of robots building each other and designing each other?
01:03:29 · Caitlin Kalinowski
[01:03:29] S0: If you're talking about robots building robots that are different than them usually, yes, I think that that's going to happen, but it's like the terms matter. I don't think there's going to be one robot that's going to build itself. I don't think that that's what it's going to look like. If you could say, "Hey, I want to build this thing and I want it to do this and this is kind of how I want it to look and here's a picture," the idea that you could, even as a hobbyist, go from a 2D picture to complex 3D CAD to assemblies to communication with vendors of how to make those parts and getting their feedback to iterating on that and doing a couple builds, that is possible, I think, in the future. Will it be good as good in the beginning as us doing it? No, but it will happen. The biggest challenge here, Lenny, is actually the data. This CAD data is some of the most valuable IP that anybody has, and Samsung or Matic, to pick on Matic, they're not going to want to give their 3D CAD to a model vendor, to a model maker, somebody making an AI model to teach it how to make great CAD. This is proprietary. This is like the secret sauce. And so, where is this data going to come from is a big question I have, which is why I think hobbyists are a more interesting place to start where they're not concerned about the sanctity of their CAD and where it goes. They don't care. They want to make something and they want help making it faster. So this is kind of where I'm interested in this starting, which is maybe a hobbyist isn't an expert in printed circuit board design. Maybe they don't care. They just want their drone to be fast and to beat this other guy's drone or whatever. This is where I think you're going to start seeing all this start, and then probably, the big incumbents are going to be slower because they have dedicated tools and a lot of IP privacy.