Navin: Thank you for being here today.
Jagdeep: Pleasure, Navin.
Navin: Absolutely. It’s a delight to be working with you after knowing you for so many years. Exciting day, launching the company and announcing a mega round.
Jagdeep: Yeah, it’s been a, a really fun ride. I want to thank you for your support. Obviously, you’ve been a critical part of both the company-building and the financing effort. But, super exciting day.
Navin: So, let’s start with what is Rhoda?
Jagdeep: Rhoda is trying to solve, what in my view, is the biggest unsolved problem in technology, which is that artificial intelligence is poised to transform the world in the symbolic AI space, but the physical equivalent of that doesn’t exist.
AI is very intelligent in terms of being able to process, words, images, and videos. But if you ask an AI to move, for example, a chess piece, it can’t do that. And despite the abundance of activity that we’ve seen in this space, in the last couple years we’ve seen, literally billions of dollars being allocated to robotic ventures.
Despite all that activity we haven’t seen today’s robot AI models crossover from being able to do demos in a laboratory setting to being able to work in the real world.
Navin: So that’s the big opportunity?
Jagdeep: That’s the opportunity.
Navin: This is your seventh company. As a founder, what was the key insight that made you jump in?
Jagdeep: First of all, on the technology side, we recognized that you needed a solution that could really cross the chasm between the lab and the real world. Crossing that chasm requires an enormous amount of data without which you can’t generalize. Basically, you have to be able to deal with a distribution shift.
The distribution you see in the lab setting is typically very narrow and very controlled. In the real world, it’s much messier. There’s a lot more variation, a lot more diversity and being able to deal with that requires generality. And the other part was on the customer side, we’re seeing enormous pressures in terms of labor and labor force issues and so on.
Is the classic thing about work that’s dull, dirty, or dangerous, right? Humans just don’t wanna do that kind of stuff, and they shouldn’t have to. The confluence of that problem where you don’t have enough workers for certain types of tasks. The opportunity to technologically cross from lab to to commercial is what creates the opportunity.
Navin: Your wedge is to go after activities which humans can’t do or don’t want to do. So this fear of job displacement is not an issue. These jobs are just unfilled and it’s really AI augmenting what I do as a human.
Jagdeep: A hundred percent.
Navin: Got it. So let’s go back. Serial entrepreneurship, seventh company.What drives you as a serial entrepreneur?
Jagdeep: Yeah,I’m very lucky to have found something that I love doing. And I shouldn’t tell you this ’cause you’re on my board, but I would do this even if it was, wasn’t getting paid in the end. The beauty of entrepreneurship is that you get to conjure up some ideas on a whiteboard with a bunch of super smart people, and if you think carefully about it and assemble the right team and, and execute well, you know, those ideas can leave the whiteboard and end up in the real world.
And the impact you can have with that can be just incredible. In some cases it can be mind blowing. You’ve seen this yourself because you’re an entrepreneur as well. I think that with every new company, there’s an opportunity to take on bigger and bigger problems. Of the selling companies I have been fortunate enough to be involved with this one strikes me as potentially the biggest.
Navin: So how big is this? Because you are saying, right, like is it in the same zone of trillions of dollars of opportunity?
Jagdeep: Well, I mean, there’s no question it’s trillions.
I mean you can count different ways, but one way to count it would be, say how much you spend every year on, on manual labor, for example, and the US for example, alone, depending on how you count, spends between $4 to $5 trillion a year on, on manual labor. And that’s just the US. If you look at worldwide, it’s more than double that.
So we’re talking about, $10+ trillion dollars.
Navin: So even if you get 10%, it’s a trillion dollars. Much bigger than some of the software markets, which are only like $600 billion.
Jagdeep: Yeah. Really in the end, what we’re doing, this is the market for work. And some people have argued, I don’t disagree, that it may be the biggest market ever.
Navin: How are you picking which segment to go after?
Jagdeep: On one hand, we talked about how this would be the biggest market ever, right? And to address that market, you want to be a generalist robot that can do anything. But it’s really important not to try to boil the ocean and try to go after that day one.
You have to have an entry strategy. Where you pick a specific problem that you can actually solve with high confidence that someone is willing to pay for. And then once you solve that the beauty of the space is that there’s a data flywheel that that gets started. So once you can deploy and perform tasks autonomously you start collecting data when you’re in the field, that new data allows your models to get better, which then allows you to address a broader range of tasks more reliably. And you can basically span the base of tasks that you address until eventually you end up being able to do the fully general tasks.
And what we’ve done is, we’re taking an approach where we’re focusing on problems in the manufacturing and logistics areas, which are problems that on one hand are relatively well-defined. On the other hand, have too much variability and diversity to be addressed with either conventional program robots or the prevailing paradigm for robots today, which is the vision language action model, the VLA.
Those models just don’t have enough generality to be able to work in the real world.
Navin: So solve real problems, narrow the use case, and then keep getting better and better and better?
Jagdeep: Precisely.
Navin: Got it. You have had the privilege of working with some of the best investors, so what do you look for in these early investors, right? Like why are they important?
Jagdeep: I think that’s critical to have really great investors, and I think the best investors are those that share the mindset of wanting to create real long-term impact. And so the things that they’re drawn to tend to align well with the things that I’m drawn to, which is, you know, how can we identify really big unsolved problems, leverage technology and worldclass teams to address them in ways where we can build large self-sustaining businesses.
So we have to make sure that the problems we’re solving aren’t just academic. But our problem is that real people, real companies, wanna pay for.
Navin: My belief is patience, perseverance is critical, but company building is a marathon. There’s no overnight success.
Jagdeep: A hundred percen.
Navin: Especially when you’re building hardware, you’re building fundamental models.
Just takes time. Other companies I’ve seen in software go one to 10, but you are a systems guy. End-to-end thinker. You go one to a hundred. Right, even in this company. And to go from one to a hundred million, what’s this playbook you use?
Jagdeep: This opportunity is, is really tremendous because you’re right, even with a thousand robots, it’s roughly a hundred million dollars a year.
But the beauty of it is as much as it’s a lot of revenue for us, it’s an equally compelling value proposition for the customer because they’re getting a lot more productivity out of these robots. And it’s a big win for both sides. I think one philosophy we’ve used relative to how we choose what to build or not build is a two step process where step one is we only want to use our scarce dollars on problems that are not already solved by the market.
If we can buy a solution, buy a component somewhere, we wanna buy it instead of building it ourselves. But secondly, if we can’t buy the component we need, we’re not afraid to build it ourselves because the problem needs solving. And we have the confidence of being able to build world class teams and in doing that, we end up finding ourselves building entire systems, not just one component. Because whatever piece is missing has to get done. And systems tend to be bigger businesses than, than components.
Navin: So it’s end-to-end thinking systems thinking.
Jagdeep: Exactly.
Navin: So reimagine things which are not solved, but then use what is standard.
Jagdeep: Yeah.
Navin: And keep innovating.
Jagdeep: Yeah. In the end you have to basically solve the whole problem. And if there’s any piece of it that’s not solved, that’s gonna impact your ability to grow.
Navin: I always like coining new terms, listening to great entrepreneurs like you, Rhoda is pioneering Labor as a Service.
Jagdeep: There we go.
Navin: You guys are going to create LaaS.
Jagdeep: Absolutely. It’s, it’s truly unreal to think about the potential impact.
Navin: Having been a stellar serial successful entrepreneur, what have been your key learnings, right, that you would be telling your younger self when you are starting your first company?
Jagdeep: Yeah. One lesson I’ve learned is that I believe that all value is created when you’re in one of the four quadrants you could be in relative to conventional versus unconventional approach and being wrong versus being right. So all the values created when you’re taking a contrarian approach. So you have to have people who are saying, um, this is never gonna work for there to be an opportunity to actually create value, otherwise everybody would’ve solved it.
So lesson there is when someone says, this is never gonna work, decouple the emotional feedback they’re providing with the content they’re providing and look at the content and see if you agree or, or not.
In the end, if you convince yourself the objections are are not real, you know, you can really take a shot and, and change the world.
Navin: Now I’ve been backing great people like you guys who are climbing and we are just the sherpa helping you guys along the way, and hopefully we are being a good sherpa carrying your backs.
Jagdeep: Yeah you’ve been an amazing partners and we’re really delighted to be working with you.
Navin: Thank you again for giving us the opportunity to be working with you. It’s been a delight and privilege and looking forward to climbing Mount Everest together.
Jagdeep: It been the pleasure.
