The Robotics Stack
00:00:00 · Oskar, Leo, Varun Nair
[00:00:00] S0: Hello. Welcome to the Robotics Stack Podcast. Today, we are joined by Varun Nair. He is scaling physical AI with data pipelines, and data seems to become the new oil for the robotics industry. You guys are gonna get a lot of valuable insights today. This is episode 20, special one. My name is Oskar. [00:00:19] S1: And my name is Leo. Varun, thank you very much for being with us today. We are super happy to have you here. [00:00:25] S2: Excited to be here. [00:00:26] S1: We are obviously going to get started with a little bit about your background, but first I would like to ask you a question that would kind of set the frame for this entire episode. What do you think is the biggest blind spot that most data buyers have today?
00:00:26 · Leo, Varun Nair
[00:00:26] S1: We are obviously going to get started with a little bit about your background, but first I would like to ask you a question that would kind of set the frame for this entire episode. What do you think is the biggest blind spot that most data buyers have today? [00:00:42] S2: So I'd rephrase the question a little bit in terms of the biggest blind spots data buyers have. It's a monopsony. It is the buyers market right now. There are a handful of buyers, whether it's your NeoLabs, your couple of robotics companies. It isn't as much that they have blind spots. It's that it matters what they want and data folks, whether it's ego data companies, other embodiments trying to like build the data or teleop data. It's up to them to sort of meet the requirements of the very few buyers that exist in the market. That being said, these very few buyers have very deep pockets. So there is a lot to be built in this industry. But yeah, wouldn't I say that there are blind spots. It really depends on how these researchers are thinking about what kind of data is going to help in pre training and post training these robots. [00:01:30] S1: I would have so many follow-up questions on this, but we are going to get started with the beginning. Tell us a little bit about yourself, your background. How did you end up in robotics data starting from the beginning?
00:01:40 · Varun Nair
[00:01:40] S2: Yeah. I mean, I have been someone who's always been intrigued by frontier technology and startups in general. I've been part of startups since I, like, working at being around founders since I was 16. And I went to Cal, did my degree in EECS. But robotics was not something I had like found as like my passion back then. It was very recently that I invested in one of my friends that ended up starting a robotics data company and we helped sell to Frontier Labs. So that's where I found my journey about a year and a half ago where I started working on robotics data. So that included building the pipeline, doing collection, ingesting all of this data, how is that actually served in our cloud storage, growing out the team, how do you deliver that data, how do you annotate that data, QC that data. There's a lot of different components that go into this, especially a year and a half ago it was quite nascent, now it's far more fleshed out. That's how I got into it. Eventually, worked in data for about six, seven months. We moved on to doing deployments and I am kind of like all over the robotics space now. Data is my entry, but been working in data, deployments, evals of data and of deployments, recent interest in SIEM. That's how I kind of got into it, it's a very exciting time to have joined, and there's a lot of people on Twitter or otherwise that I've learned from, and I'm grateful for that.
00:02:56 · Leo
[00:02:56] S1: And we definitely learned a lot from you both on Twitter and during the several calls we had. Yeah. I mean, I saw you also worked at Coinbase for a little while. You were kind of in crypto, we can say, and you ended up in robotics. What from being an engineering student drove you through crypto and then to robotics, for example?
00:03:15 · Varun Nair
[00:03:15] S2: I think there is actually a very common thread here, and you'll see this with a few different folks. A lot of people that were in crypto are finding their way into robotics, whether it's data or at a different level. And I think it's something in the psyche of people who are chasing the future, and SF is a great city for that. But in general, the goal is that, hey, crypto made a promise that the financial system is going to be changed, be decentralized. The promise was that there's a better tomorrow out there. And I think it's similar with robotics that this techno optimism sort of tech progress that can be created with newer technologies is inherent. So it's the same reason I enjoyed learning about and building in crypto. And it's the same reason I want to do robotics, and it's the same reason why I'm excited about space exploration in the next twenty years. So it's the same muscle of techno futurism, techno optimism around how these technologies are going to help us go forward as civilizations. That being said, more grounded answer around Coinbase in specific, I learned a lot there. I was actually doing within security org, I was doing a lot of data engineering, which gave me the building blocks of understanding how like data flows at like an enterprise company. How does that even look like? What the technologies are? And it was right out of college, so it was pretty early on and I learned a lot that ended up becoming very transferable and applicable in the future when I was working with robotics data.
00:04:39 · Oskar, Varun Nair
[00:04:39] S0: Yeah. Crypto is a very universal technology because it's finance, of course. Do you already see some potential use cases where robotics might be even connected to crypto? I think we have seen PrismaX. I'm not sure if you know them, but I think they dabbled in crypto for a bit, tried to create something. We've seen it in AI. You know, sometimes it's a bit of a let's just merge crypto. For the sake of it, do you actually decent potential there? [00:05:05] S2: I think it's a bit forced right now. I do think there is potential there, but to be very honest, I think current implementations are either forced or too nascent. There are examples of how you can use like decentralized systems for verification, like of work, which can be used for, let's say, distributing and getting data. There are a couple of companies I know that could be using DataQC through a certain kind of blockchain like system. That being said, it's not needed. It's novel, it's cool, it's exciting to my student like mind, but I wouldn't say it's like something that economically makes sense, and someone can make sense of it, but I haven't seen something like that actually pop off so far. [00:05:44] S0: Yeah. Neura Robotics, for example, from Germany, they raised with Tether. Somewhere I read about it that they're kind of planning to put a wallet essentially on each robot, but as you say, you know, it's very far into the future that I see a robot working or going somewhere and paying with their undevised wallet or something like that.