Dream Machines: Physical AI, Robotics, Being Human
00:02:48 · Charles Wong, Evan Helda
[00:02:48] S2: About a little more than that. But, like, you know, the the... You know, when we started the company, it didn't feel like we timed it just right. Right? Like, it felt like we were too early. I remember we're pitching our first, like, round of VCs. A, what... They were asking about is AI. B, oh, oh, mean robotics? Robotics sounds very uninvestible. You're not even building robots. You're a dev tool for robots. And you're like, oh god. Okay. We'll get there. We'll get there. Right? And now it's paying off. So... [00:03:18] S1: I think that's part of every major success story is that they... When they first launched the business, it does feel a little bit too early. But the founders had the vision. They had the instincts that they were just early enough that there's gonna be the ability to... Opportunity to build the foundational capabilities to be paddling for the wave when it when it rises versus having to catch up to it. So kudos to you for that. Before we jump in here, what would make this a home run for you guys? I'm excited to share this out with your friends, family, colleagues, investors. What story? What points? What ideas do wanna land?
00:03:18 · Evan Helda, Aravind Kandiah
[00:03:18] S1: I think that's part of every major success story is that they... When they first launched the business, it does feel a little bit too early. But the founders had the vision. They had the instincts that they were just early enough that there's gonna be the ability to... Opportunity to build the foundational capabilities to be paddling for the wave when it when it rises versus having to catch up to it. So kudos to you for that. Before we jump in here, what would make this a home run for you guys? I'm excited to share this out with your friends, family, colleagues, investors. What story? What points? What ideas do wanna land? [00:03:57] S0: I think maybe one of the key ones is, like, what does it actually take to deploy robots? I think that's, like, the unique insight that we have. Like, there's, like, two groups of people. Right? There's the the folks, you know, in the labs. You know, now this year, they're starting to, like, slowly deploy. But deploying robots into the wild, what does it, like, actually take to deploy those robots? And I think, you know, being able to share that perspective with a lot of the customers they're working with and also, like, a lot of the investors and, like, hey, these are the... That unique perspective and, like, the edge and insight that we're seeing from all our customers who are actually deploying robots into the wild. I think that could be an interesting take. Charles, anything on your end?
00:03:57 · Aravind Kandiah, Charles Wong, Evan Helda
[00:03:57] S0: I think maybe one of the key ones is, like, what does it actually take to deploy robots? I think that's, like, the unique insight that we have. Like, there's, like, two groups of people. Right? There's the the folks, you know, in the labs. You know, now this year, they're starting to, like, slowly deploy. But deploying robots into the wild, what does it, like, actually take to deploy those robots? And I think, you know, being able to share that perspective with a lot of the customers they're working with and also, like, a lot of the investors and, like, hey, these are the... That unique perspective and, like, the edge and insight that we're seeing from all our customers who are actually deploying robots into the wild. I think that could be an interesting take. Charles, anything on your end? [00:04:40] S2: No. I I think one one is that, like, Evan, to your first point, like, like, we've been here, we've been building this and thinking about this for a while. Right? We're not just Gen AI groupies who jumped on when, you know, foundational models became a thing. Very much very much [00:04:58] S1: Didn't say groupies. I'm gonna steal that term.
00:04:58 · Evan Helda, Charles Wong, Aravind Kandiah
[00:04:58] S1: Didn't say groupies. I'm gonna steal that term. [00:05:00] S2: We've been thinking about this since we were in college. Right? And like working on it in various forms. Right? The other thing, you know, like, think to Aravind's point as well, the... We have a unique vantage point where because we work with so many different autonomy teams, we see a lot. Right? And we see the common failure modes, we see the success paths. And that's an interesting place to be as a a SIM tool, as a dev tool across the stack. Right? So just so I think we want people to know that about us. [00:05:34] S1: Yeah. That's awesome. Let's dive into all that. Step into all that. Double Right, [00:05:38] S0: clicking to what Charles said. It's like every customer deploying robots, they just fall into the same traps. Yeah. And it's like, for us, it's just like, guys, you will fall into this. Just don't fall into it. It's like, no. But I wanna try it. And then they're gonna, like, fall into the trap. It's like, you know, if we can, like, hey, guys. These are the the common failure modes. Avoid them the best you can and, like, do these things right before. So it's like just, like, helping them course correct earlier. Because we just see these failure modes again and again and again at all stages. You may be a hyperscaling startup that raised, like, $2,000,000,000. You may be a giant MNC. You may be a small startup. They all make the same mistakes, which is crazy.
00:05:38 · Aravind Kandiah, Evan Helda
[00:05:38] S0: clicking to what Charles said. It's like every customer deploying robots, they just fall into the same traps. Yeah. And it's like, for us, it's just like, guys, you will fall into this. Just don't fall into it. It's like, no. But I wanna try it. And then they're gonna, like, fall into the trap. It's like, you know, if we can, like, hey, guys. These are the the common failure modes. Avoid them the best you can and, like, do these things right before. So it's like just, like, helping them course correct earlier. Because we just see these failure modes again and again and again at all stages. You may be a hyperscaling startup that raised, like, $2,000,000,000. You may be a giant MNC. You may be a small startup. They all make the same mistakes, which is crazy. [00:06:20] S1: Well, let's keep pulling that thread because I think it's a really interesting place to maybe start. What is that most common failure point? Is there one big failure point they're all repeating? Is there a a basket of them based upon certain verticals? What what are you guys seeing currently?
00:06:36 · Aravind Kandiah
[00:06:36] S0: The biggest one is actually how these robotics teams think about testing. Right? So the the very traditional... There's two groups of folks. Right? One group of folks is like, okay. We're gonna just go out and collect as much real world data from some kind of test platform. Right? Maybe it's a humanoid. Maybe it's a a Yam arm. Maybe it's something in a very controlled environment, and then they'll use that as their, like, test set. The other one is they just say, okay. We're just gonna manually roll this out and get a human babysit the system while it's rolling out these policies. And then they will get, like, a certain a certain score from this. Like, you know, maybe it's, a 80% success rate. And then they will then take that 80% success rate, then they'll go to their management and say, like, hey, guys. You know, we think we have achieved, like, a significant level of performance. Let's start rolling this up. But they don't have the discussion, which is, like, setting the expectation of, hey. These are all the scenarios that I need to handle in the actual wild and discussing them with the end users. Right? So let's just take, for example, let's say you are doing industrial manufacturing. Right? You need to be able to pick and place very specific parts and put them on, like, specific places in the factory. There's actually a a finite number of scenarios that you need to be able to handle, but most teams only test, like, maybe 30% of them before actually going out into the wild. Mhmm. Because they just don't have, like, the ability to test the other 70%.