Digitized | Spotlight
00:01:14 · Winnie, Michael Cho
[00:01:14] S1: To kick it off, Michael, can you share a little bit about your journey into robotics and crypto, the founding of BitRobot, and kind of what led you to the kind of the early days and inspiration behind BitRobot? [00:01:26] S2: Yeah. Well, first of all, thanks for having me. Yeah. So honestly, I got into it just kind of by accident. So this is during pandemic. Me and my brothers were like, we were all kind of separate in different countries, know, and it was like the end of the world. We thought we needed a reason to keep in touch. So we thought we should just geek out over something and then we decided to just build basically a sidewalk robot. And so I think for like nearly half a year, every weekend we spent the entire Saturday like hacking on something remote. I mean I'll have my own robot, my brother will have my own robot. So we kind of did that. [00:02:05] S1: Yeah, that's cool.
00:02:05 · Winnie, Michael Cho
[00:02:05] S1: Yeah, that's cool. [00:02:07] S2: It was just a weekend hacking project but then me and Sam, my second brother, we thought that that's something that we could offer that have never really been tried before because at that point even I would say now like most of the cyber robots are pretty expensive and so we thought we could build something that's way cheaper and the way to do it is to build it in a way a dumb robot and yeah, and we basically kind of think of it as a toy, but our hypothesis was that the data that you can gather from such a dumb robot is probably as valuable as something that you can gather from a robot that costs like $10,000 or something like that. And my thought back then was that a lot of these models embodied AI model, like... Or if you want to do autonomous driving or these cyber robots, it's probably, like, better bound... Like, bottleneck by data. So if you can just drastically reduce the hardware cost, then it's it's gonna help a lot. But to be honest, like, me and brother, like, it was pure naive optimism. Have never So [00:03:19] S1: almost like how it works though, you have to be quite naive to kind of build something like this, you know?
00:03:19 · Winnie, Michael Cho
[00:03:19] S1: almost like how it works though, you have to be quite naive to kind of build something like this, you know? [00:03:24] S2: Yeah. I mean, was a hunch. I mean, I I was keeping track of what's happening in the research world. I mean, even back then I used to read a lot of research paper, especially in computer vision and all that. So I kind of knew things were heading that way, but it's still, I mean, me and my brother, we are not both this training, So we just... But we were just very curious and we all like, we like to hack stuff. Mean, it's a Wiki that way. So it started like that. [00:03:56] S1: I love that. In the early days, in the early days, where were the robots that you guys were building over on the Saturdays and nights and weekends? What were, like, the first iterations of what you and Sam were doing?
00:03:56 · Winnie, Michael Cho
[00:03:56] S1: I love that. In the early days, in the early days, where were the robots that you guys were building over on the Saturdays and nights and weekends? What were, like, the first iterations of what you and Sam were doing? [00:04:08] S2: Oh, you mean where... Did you say where were the robots? Oh, what were the... I think it's all somewhat robots, but they they have, like wires dangling out all the time, they barely worked as you can imagine, they are overweight, very you know a lot more expensive than what we have now. So I think I would say we've come a long way, yeah but it was all just learning by doing kind of thing. I mean on one hand I think it's bad in that we, to be honest, we didn't know what we're doing at the beginning but on other hand it was also very good in that me and Sam, we had no preconceived notion of what should or shouldn't work. It was all just a blank piece of paper, we just assume we're idiots, we don't know what we're doing. But our philosophy was always just to test it in the real world, possibly because we don't have a proper lab anyway, so we just put the robots out there and see how they break And then... But we always made a conscious... Well, from day one, we made a conscious effort to gather the data and that has worked wonderful for us down the road when we started having more partnership or that. Yeah, it started that way. It's just a weekend project. Yeah.
00:04:08 · Michael Cho, Winnie
[00:04:08] S2: Oh, you mean where... Did you say where were the robots? Oh, what were the... I think it's all somewhat robots, but they they have, like wires dangling out all the time, they barely worked as you can imagine, they are overweight, very you know a lot more expensive than what we have now. So I think I would say we've come a long way, yeah but it was all just learning by doing kind of thing. I mean on one hand I think it's bad in that we, to be honest, we didn't know what we're doing at the beginning but on other hand it was also very good in that me and Sam, we had no preconceived notion of what should or shouldn't work. It was all just a blank piece of paper, we just assume we're idiots, we don't know what we're doing. But our philosophy was always just to test it in the real world, possibly because we don't have a proper lab anyway, so we just put the robots out there and see how they break And then... But we always made a conscious... Well, from day one, we made a conscious effort to gather the data and that has worked wonderful for us down the road when we started having more partnership or that. Yeah, it started that way. It's just a weekend project. Yeah. [00:05:17] S1: Cool. I love that story. I guess, like, in getting started, what were some key challenges or bottlenecks that you faced in the early days to kind of getting some of these general purpose physical AI and how does, like, BitRobot help solve them?
00:05:32 · Michael Cho
[00:05:32] S2: Yeah. So like I say, I think a big thesis of mine even like, I would say nearly four years ago when we decided to really do it full time was data is gonna... Like real world data could be a big big part of of solving Embody AI. And so you asked about, like, how we got into crypto, like, how has crypto involved? And so to be honest, when we... Me and Sam first started, I I thought, like, most skeptical crypto is scam. Like, I was very skeptical about crypto to be very honest. But then I discovered Helium. Like, this is... I remember... Yeah. This was, two months after starting the company. At the time, was only me and Sam. Right? And so yeah, and then Helium just totally blew my mind. I thought it was crazy that you can crowdsource both the hardware cost as well as the actual deployment across the world. There they were selling you know, hundreds of thousands of units, I think, by by that point, you know, at at about $500. And we were also trying to get our robots down to $500. And I wanted that that that hidden map that hidden had, and and so I thought I I need to learn about that. And I would say fast forward to now, I definitely 100% believe in VPN if it's done well. I think some things you can scale a lot faster using, you know, this kind of permissionless incentivization scheme. And I think especially for robotics in... Especially in in the research world, they call this like in the wild data, which is, you know, the the data is collected literally where the robots are supposed to be, right, instead of being in labs and warehouses and and things like that or a a controlled environment. So... And this is very critical because unlike the older days when you do, like, what they call classical robotics, meaning the robots are... These are robots that you'll probably find in factories, you know, like, they are doing the same thing day in, day out, like, you know, thousands of time every, like, every month. So you can use very deterministic kind of, like, coding to get that... Get those work, and those work very, very well. But those don't generalize. Right? But if you use the new paradigm, you know, the most extreme is you train thing what they call end to end. Ultimately, these AI models is just a reflection of the data that you collect from.