What's Actually Holding Humanoid Robots Back Isn't the AI | Andrew Kang, RoboStrategy

1. 0:00 – 1:15

   Intro

   1. SPSpeaker0:00

      [gentle music] The amazing thing about the Figure AI live stream was it showed that this is real. It's not a video where they took 100 attempts at doing a task and they showed the best one. This was a live stream that went on for eight or 10 hours, and it ended up going on for eight days. What was funny was the, the human actually won. It won by a little bit, but, you know, at the end of the day, the intern that was doing the challenge, his hands were blistered. He was not having a lot of fun. It was exhausting. It's not something that he'd probably wanna do again. I actually think it's closer to something like two to three years where humanoid intelligence, robot intelligence gets good enough to do most of the tasks that we need on, on a daily basis. This is a technological revolution that is different because it turns physical labor into a, in a product that almost anybody can access. Hey, guys. I'm Andrew. I'm the CEO of RoboStrategy. RoboStrategy is one of the first publicly listed venture funds on Nasdaq, and we're the only publicly listed venture fund that is exclusively focused on investing in robotics and physical AI. We're invested in quite a few robotics companies, Figure AI, Apptronik, Dino Robotics. We also have Standard Bots in the portfolio. They build industrial arms, cobots, and also companies like Path Robotics that focus on specific tasks like welding.

2. 1:15 – 6:00

   The Bet: All in on Figure AI and the full-stack future

   1. SPSpeaker1:16

      One of the largest investments that we ever made into the company, Figure AI, it wasn't a consensus investment because everyone that we had asked, the other venture investors that were more familiar with investing in, in frontier technology, they didn't really believe that humanoid robotics was gonna work anytime soon, or they perceived there was gonna be a lot of risks. They saw that humanoids or, uh, companies building a robotic space had never produced big venture scale outcomes, as opposed to understanding the context that things were changing and that technology for robotics was gonna be accelerating and moving at a different pace than it was before. And so that's why we really decided at that point to pivot, you know, our entire company into focusing on investing in robotics. It's funny because when I went and invested in Figure for the first time, I had never actually been to their facility. I watched every single video I could of Brett, of Figure, um, and you know, all the work that they had done for previous companies as well. Just kind of doing our research on the team, the founder, it was quite clear that this is one of the few teams that were able to do it. They had the background in hardware engineering. They had the background in robot learning. They had the background in all these really niche fields, like hand engineering or robot controls and fleet management. Looking at all the competitors and all the other players in the space, it was pretty clear that they were one of the top teams to be able to, to accomplish the task. We're not the type of investors to be very dogmatic. When we believe one thing, never change our minds. High conviction, strong beliefs loosely held, um, but we're always trying to reevaluate our beliefs of the world. And if there's important information that comes up to lead us to believe we're wrong, then we're happy to change our minds. And it's important for us to always track the pace of development across all robotics companies, not just the ones that we're invested in, so that we can understand how are the different companies stacking up against each other? How is the field developing across all the different characteristics that we look for robotics companies, right? How are different players scaling up their robot fleet? Um, how are they conducting robot learning research? You know, what are they doing on the hardware development side? And from all those kind of points of view, we still believe Figure is one of the top companies. Really it's, it's them and, and Tesla Optimus at the top. We're really excited about the vertically integrated robotics companies. These are the companies that we're investing in the most, and these are companies that are not just building their own robot intelligence, but they're building the hardware, uh, they're doing the deployments, and they're also, uh, scaling up their own manufacturing capabilities. When we think about why these companies exist in the first place is because when you're training the robots, it also makes sense to be able to, you know, have built the robot hardware yourself so that they're co-optimized for each other. Maybe a robot that has better torque sensing within its joints, uh, is able to be better modeled in simulation, or you can build a, a model that incorporates that type of data that you're capturing. So there's a lot of advantages in building these systems in parallel with each other, um, because it makes the training more efficient, research more efficient. At the end of the day, the robots are gonna be more performant as well. One of the key data pieces that are required for robot learning development is the actual robot data itself. Robots that are either doing a specific task, running a model, or robots that are controlled using teleoperation to collect the data. One of the ways that you can think of this is if you were transformed into the body of somebody that was seven foot tall, you probably would have ... be a little bit awkward in interacting with the world around you, as opposed to you continuing to interact with the world around you in your current body, in your current physical form, because that's the body that you're used to. And so having that embodiment-specific data is gonna create more effective models. And to be able to collect a lot of embodiment-specific data, you're also going to ne- need a lot of robots. That is one of the bottlenecks that the industry is currently working through right now is if you're trying to buy 100 robots or 1,000 robots, that's gonna be pretty tough. You can't get that in a day. You need to make those orders ahead of time, and it's gonna take time to produce those robots. And so if I have my own manufacturing facility, I can earmark all of those robots just for the sole purpose of collecting data myself, and that's what companies like Figure are doing, what companies like Tesla Optimus are doing, Apptronik as well. So I'm not gonna have a bottleneck because I don't have to worry about, say, a robot company supplier in China where I'm getting my robots from just not having enough available because demand has skyrocketed. And that's what you've seen with GPUs or, you know, other components of the supply chain, is that things ... Demand for a lot of these items are scaling up really, really quickly, and it's hard for these supply chain vendors to be able to produce them enough to fill that demand.

3. 6:00 – 10:25

   The Future: How far humanoids actually go

   1. SPSpeaker6:00

      [gentle music] I think the market for humanoid robotics is gonna be in the tens of trillions. It's a crazy number. I think the way that you can get there is you can take two views. You can take the top-down view, which is you just look at all of the market for physical labor in the world, and that's a $50 trillion market. But it's, it's a little bit hard to conceptualize. And so the way that we thought about it was, imagine one humanoid. It might be sold or it might be s- leased for $50,000. That's, that's a pretty good price because a laborer in the US or physical worker in the US, you have to pay maybe $50,000 a year when you're considering all of the benefits and, and all-in costs, or sometimes more than that. And then you take that $50,000 and you multiply it by 100,000 just as a starting point. That number is already $5 billion. A company that's making $5 billion a year is, is a pretty sizable company. But then, right, you just scale it but up by 10, and you say, "What if I have a company that sells a million humanoids per year?" It's $50 billion. We make billions of cell phones per year. We make hundreds of millions of cars and PCs. And so I think we're probably gonna make a lot more humanoids. And so you can really clearly see that there's a, there's a trajectory for this industry, for humanoid robots to get to trillions of dollars of revenue, and that would imply tens of trillions of market cap. And that's almost an underestimate because when we start making labor more abundant, uh, more affordable, then it expands the market as well. We can start sending robots to space. We can start sending robots to build more data centers, right? That is kind of a key constraint to the data center build-out right now. It's not the things that go into making them, it's the, the labor. It's the people that are actually putting things together, doing the plumbing, electricity. I actually think it's closer to something like two to three years where humanoid intelligence, robot intelligence gets good enough to do most of the tasks that we need on, on a daily basis. So I think you could almost characterize this new wave of robotics as almost the fourth industrial revolution, this wave of robotics and AI. We've created machines that allow us to produce many different things and to make the everyday life easier. But this one is really different because this is the first time that we've been able to create machines and intelligence that can really do anything a human can do. And that opens the door for a lot of different things that weren't possible before. If labor gets as cheap as, say, $2 an hour, or it just becomes a product that we can buy, so every single person in the world, they can have a personal assistant like everyone has their own iPhone. People can also buy robots or rent robots to maybe even produce things or to build companies that previously maybe they couldn't afford or maybe they couldn't find the right people to do. This is a technological revolution that is different because it turns labor, physical labor, into a, in a product that almost anybody can access. AI research has really been accelerating. When you think about research, right, AI development, it's not something that is on a, on a slope that is completely flat. It's something that changes, and it feeds back on itself because the better AI models get, the more of AI research can be automated, the faster it can be done. Loops that were previously required a lot of humans can now be running, right, 24/7/365. And they're also able to process a lot of information a lot faster. And so a lot of that, uh, what you consider efficiency gains is also gonna be applied to robot AI research, and that can exist across multiple di- dimensions, right? It helps with the actual speeding up of the research, but there's also a lot of innovation and learnings from AI research that can be applied for physical AI research. Learnings in how to best do data annotation, infrastructure around collecting data and annotating data, learnings and innovations on how to structure mid-training, on how to do reinforcement learning. A lot of the same concepts from LLMs can also be applied to physical AI models. And so that's why I think the amount of time for these models to get really good is, is probably a lot faster than people think. But at the same time, the models are gonna get really good, but that doesn't mean we're gonna have robots doing all of that work in the next two to three years because even though the intelligence can get there, we're still gonna have a bottleneck with manufacturing. I can spin up a million instances of a chatbot instantly, but I can't do that for robots. I can't produce them out of thin air. And so we're gonna need to scale up all the factories. We're gonna have to scale up the supply chain for all the components that go into a robot, and that's gonna take some additional time.

4. 10:25 – 13:19

   The Shift: Why Open Source wins

   1. SPSpeaker10:28

      [gentle music] One of my views is that open source models are gonna get really good. Two or three years ago, open source models were probably less than a few percentage of all tokens that were produced. Nowadays, open source models produce something like 25, 30%, maybe even more of all tokens that, that are produced. They're getting really good, and they're also saturating benchmarks. And so the gap between open source and frontier models, it used to be around two years. That was a few years ago. Now it looks something more like six months. We're going to get to a point where the open source models start to saturate the benchmarks. Even though there might be a gap between open source and frontier, that gap may not matter for a lot of tasks in the world. Because if I'm doing a simple task, like for example, restocking shelves or, uh, you know, assembling a computer mouse, I don't need a really high-level intelligence to do that. I don't need an Einstein to be able to do these tasks. And so as long as these open source models get to that level, which I believe they will, the model layer will almost commoditize for physical AI. We're not gonna be there yet, but I think that's somewhere, something that we're gonna get to in somewhere maybe the next three to five years. And so I think at that point, intelligence, it becomes really cheap. What I consider, you know, the most valuable companies or the most important companies are probably gonna be the ones that are doing deployments, they're, they're producing the hardware, uh, or, you know, they're innovating on new designs or components to make these robots even better. NVIDIA is also a very big player in open source model development. NVIDIA, if you look at them, uh, they're producing open source models for just general, um, you know, LLM software engineering. Nemotron, they're really climbing the benchmarks. They're producing open source models for autonomous vehicles, and they're also producing open source models for physical AI and, and robot intelligence. And, you know, some of the best researchers in the field are, yes, they're across some of these closed source labs, but they also are at companies like NVIDIA. I think everyone needs to keep in mind... Is that for NVIDIA, they're one of the most powerful companies in the AI space. They have a lot of resources. They have a lot of really smart people. It is an- almost an existential threat for closed source models to win. Because as you saw with Anthropic starting to train on Google TPUs, if companies decide to optimize for and train on other hardware, NVIDIA starts to lose their business. It becomes a bit of a threat to them. And so that's why they're putting so much effort into developing their own open source models like Nemotron, like the autonomous vehicle models, like, you know, all the different physical AI models that they're developing, Groot, Cosmos, Dream Zero, et cetera. That is something that I, I feel like can't be understated because it's... If you're building just, you know, physical AI models, you have to think about, "I'm competing with one of the best AI companies in, in the world."

5. 13:19 – 15:33

   US vs China: Why it's not a race

   1. SPSpeaker13:24

      I think some people like to frame this as US versus China. I think both industries are gonna be massive in the future, and I think they're both independently going to build really great hardware and, and robot intelligence. Industries are going to develop a little bit independently in the sense that the robots that are sold and they're used in America are probably gonna come from American companies, and the robots that are bought and used in China, they're gonna come from Chinese companies. The world is kind of coming to a place where a lot of countries, they're interested in independence. They wanna produce things in their own country. They don't wanna be dependent on another country. They wanna make sure that on their own they can survive and they can thrive. And so there's a lot of interest right now in the governments from both China and America to really accelerate the development of robotics in the, in those individual countries. Some of them, uh, like China, they've invested many billions of dollars either directly or indirectly through government funds and municipalities. And in the US, that hasn't exactly happened yet, but I believe, believe we're gonna get to there in the future. The US has already shown that they're interested in funding domestic companies. They've funded and provided financing to rare earths processing companies, directly invested in semiconductor companies like Intel, and I think it's pretty clear that there's a similar amount of support that's gonna come to the domestic robotics industry in America as well. It is true that the US is somewhat ahead on the physical intelligence models. At the same time, there are some really great research groups in, in China, some that are associated with Alibaba, for example, that are building robot models that are pretty close to the frontier. They have really smart researchers there, and there's also really smart researchers in America as well. Eventually, both countries are gonna get there, probably independently, but also they're gonna collaborate in, in doing so because there's a lot of open source research that's published, research that helps both countries. And so I wouldn't really think about it as, as a race or, you know, one's a little bit ahead and one's a little bit behind. I, I think it's really kind of short-term because I think at the end of the day, in five years from now, 10 years from now, both countries are gonna be able to get there themselves.

6. 15:33 – 21:09

   Where to build now: The white space of robotics

   1. SPSpeaker15:37

      In terms of where people might wanna build, I think there's so much white space. Because there are hardware platforms that exist, it makes the development a lot easier for someone that wants to build for a specific application. And so I think you can really think about robots as kind of like the smartphone. Apple, right, they make the iPhone, but there's this whole developer community that exists outside of people that are just building applications, people that were building applications for time management, for taking notes, et cetera. That can also happen for robotics where maybe I want to build, uh, you know, robot applications to teach robots or the skills on how to cook really well or maybe how to do elder care. Or maybe I wanna build a robot application to help, uh, increase, uh, you know, the efficiency of certain farming standards or, uh, agriculture techniques. I mean, you can really think of anything where physical labor is involved as a potential robot application that can be built. That, that is, you know, such a large white space. A company that we haven't invested in but we're watching quite closely is, is Unitree and also, you know, other Chinese companies. A lot of these companies, what they do is they haven't- they don't take the same approach as the US companies. Where a lot of the US companies, they wait until they have the product that's perfect, that they're ready to basically sell into, you know, the home or a factory environment and, and it works absolutely perfectly. The approach that the Chinese companies are taking is they're releasing their hardware, their robots, as more of a platform for research or entertainment for people to build on. It's not necessarily the case where I can buy a Unitree robot and then it- it'll immediately be able to do everything I want it to do. But it's also a pretty interesting business or commercial strategy because now that the robots are out in the world, you get a little bit of, uh, of, of a developer mode. You get a little bit of a deployment mode because people then become comfortable with using those Unitree robots, right? There could be developer tools. There could be data collection platforms that are specific to the Unitree robots. If people are doing a lot of robot-based data collection, that might be now Unitree-specific. And so if you're building models that use a lot of this Unitree-specific data, those models might run better on Unitree robots as opposed to other robots. And so I think that's a pretty interesting strategy that we're not seeing too many companies in the US take. There's one company in our portfolio called DexMate that is selling robots, and you can consider them using a similar strategy. But that, I think, is a pretty interesting approach that can result in a lot of other maybe downstream effects because now that everyone has access to these robots, other people can build applications on them. And so those applications don't necessarily need to come from the f- the company that's building the robots. It can come from outside researchers or other startups that just wanna focus on the AI side of things as opposed to building the hardware and figuring out the manufacturing component themselves. I actually think the industry's really early, so I don't think anybody's missed anything yet. Because you look at the robots today, and they're getting a lot better, but they're nowhere near, if you, for example, Opus 4.8 or ChatGPT or any of the LLM models, right? Where they're actually doing a lot of the work that humans would do, and they're being used across almost every co- company in, in the world today. We're not there for robotics, and that's what makes it a really exciting time as well, because there is st- still a lot of opportunity for people to join really exciting companies that have a great growth trajectory or start making investments in the space themselves. I, I think the first thing to do is, is really just start doing more research, talking to friends that might be working in the industry. If you really want to, right, be involved in some way, either by joining a company, starting your own company, or investing. It's pretty underappreciated how hard building a robotics company actually is. I'm seeing online these days a lot of people from different industries saying, "Hey, look, I'm gonna go out and, and start a robotics company." We're real excited about the industry, and we think there's gonna be a lot of great companies that come out of this, a lot of great technology that comes out of it, but it is also really hard. The amount of kind of, uh, knowledge that you need to kind of accumulate over, over many, many years, amount of experience you need to have, right, like this is not building a software company. To be able to understand, uh, you know, all the components needed to build a successful humanoid company or a robotics company from, you know, mechanical design to electrical engineering, high-rate manufacturing, how to actually deploy the robots in the real world, it's a little bit maybe, uh, underappreciated. There's gonna be a lot of maybe investment that goes into this space. There's gonna be a lot of startups that go out. But I would maybe caution people to, um, just appreciate a little bit more how, how difficult it is. And you're, you're gonna need a lot of real experts, people that have years, decades of experience in this space, people that have had a significant amount of experience working at other real, you know, manufacturing or robotics environments before, to be able to really build a successful company. [gentle music]

Episode duration: 21:10