Siddhartha Ahluwalia 0:03

Hi, this is Siddhartha Ahluwalia. Welcome to The Neon Show. I’m your host and managing partner of Neon Fund, a fund that has invested in some of the best enterprise AI companies between US and India corridor, like Atomicworks, Spotdraft, CloudSEK. So glad to be doing podcast with you, Mehul and Navneet. Thank you for being here today. When did you quit your job at Google to start Matic?

Navneet Dalal 0:26

So we left in early 2017, Mehul left, I think it was March or April in 2017, I left in the July of 2017. But the whole point was that Matic for us is our last company that we want to work on. We envision that we want to work on it for 20, 30 years and not get bored. So we picked a very ambitious problem that, hey, we want to make sure that we can genuinely add, save people time and save people effort through autonomous robots in homes, in indoor environment. And that’s the overall theme. And we knew this is the, having worked at like, you know, so we were after acquisition of Flutter in Google Research, we spent a year or so in Google Research and from there moved to Nest to work on Nest cameras and Mehul left the Nest camera portfolio there. He worked on the Nest camera, three generations of Nest camera, I worked on Nest camera algorithms. And through this experience, we did ship the first deep learning algorithm in the wild in 2016 on Nest cameras. I also got an opportunity to work on the Coral TPU chipset from a specification point of view for Nest purposes. And we saw that, okay, the camera, the computes are there, the algorithms are happening, and now would be a very good time to go and create a different kind of an opportunity because Nest was in the IoT devices at home, and, but homes have so many other problems. Like, you know, if you start doing the chores at home with the young kids at home, doing dishwasher, washer, cleaning floors, then the drying, like washing clothes, et cetera, then keep tidying up after the kids, all of these become huge, gigantic efforts. And so that was our notion that, hey, we’re going to save people time and effort. The idea to put our own money was also that, look, this is our company, which is we want to put for 20 to 30 years, like, you know, this notion of skin in the game from Nicholas Taleb, that like, hey, you need to have, make sure that if you do fail, it hurts. So we, this is back in 2017, the whole idea was that we’re going to solve the problem using cameras. And if you remember, self-driving cars, they were all putting all kinds of LIDARs, time of light, radars, et cetera, kind of sensors. So it wasn’t really obvious. If you look at what we, Matic, does today, that you can indeed solve the problem using cameras alone. So we spent first year and a half just building the technology to prove to us, forget anyone else, prove to us that we can solve the problem using cameras. And it’s only when we got it working enough, we have done all the demos of the core critical vision, that this is how it should work. This is the kind of experience we want to provide. That’s when we went out and raised our angel round. So first, Mehul mentioned earlier, million and a half, we just first 18 months just to bootstrap, to build the demos. Then we raised our kind of, you can say, angel round. And then we build a prototype of the product, that this is what the product would look like, this is what it would do. And then we did the next funding round. And after that, this long slot of just getting the product working, which was perhaps also the hardest time in terms of like, you have to just keep your head down and continue working on the product.

Siddhartha Ahluwalia 3:53

Yeah.

Mehul Nariyawala 3:54

Robotics is hard.

Siddhartha Ahluwalia 3:55

And you mentioned in an earlier conversation that you put 35 million in Matic, combine both of you. Yeah.

Mehul Nariyawala 4:04

Yeah. That one, we got lucky. So, you know, part of the reason is, I think we touched about it a little bit earlier that we heard from a lot of second time founders, as we’re thinking about doing it second time, that it’s always harder to raise funding, but second time is slightly easier because based on your predigree, they might give you at least angel investors will come in slightly easily. So that kind of gives you a feeling that, okay, your idea is really vetted out, but it’s usually not the case. So we heard that, okay, before you do anything, make sure you talk through your point of view. So then we just said, okay, let’s make sure one of the criteria was let’s make sure there is skin in the game that just like Flutter here also, we create a situation where there is no plan B that is only planning, like put all the chips on the center of the table, so to speak. So we bootstrapped initially and invested our million and a half hours before and built a demo and showcased the technology potential in 2019, and then went out and raised our staff. No, but I do remember on day one, we didn’t have an office, I had a spare room on a loft in my home, and Navneet came in and first thing he wrote down on a piece of paper was that whatever we do next, we are not going to sell it, so not for sale. And then we pasted it up there. So that was the idea that this is the last job, this isn’t something we’re never going to sell. And then we believed in a few different things. So this time around with Flutter, we started working on it, but we hadn’t really thought through what the long term potential was. So we always knew what we’re going to achieve for the next 12 months, but not beyond that. Versus with Matic on day one, we had a plan for like next 30 years, so we had thought through everything. And then there were sort of rules of engagement, one of them was screen of skin in the game. The other one is we had been in computer vision, so let’s make sure we do computer vision as a core sort of strength. So that also worked very well in our favor, because one of the thesis was that indoor world is built by humans for humans, for eyes, two eyes and brain-based perception system. So shouldn’t indoor robots have it? And this was also around the time, I don’t know if you had visited back then, but if you came in 2017, you would probably find 200 self-driving car startups in Silicon Valley. So self-driving cars was all the rage, right? So it’s like at some point, Navneet and I were like, wait a minute. So there are 200 plus teams who think they can build cars that wouldn’t get into an accident and be safe. But there is not a single team that thinks they can build a robot that does not require bumping in our homes. That doesn’t make sense. And there has to be something that we do. And that sort of led us to this idea that wait, if level five robots can exist on the roads, why can’t they exist in an indoor environment? And why can’t they exist in homes? And what does that even mean? And then we’re like, okay, if level five robots for cars means that cars drive like humans, then level five robots for indoor world and homes must be that they behave like humans, navigate like humans, avoid obstacles like humans, clean like humans, manipulate like humans, do all chores like humans. So how do we go solve that? And this is probably the right time to go do it. So that’s how I started thinking.

Siddhartha Ahluwalia 7:15

Right now, cleaning is the use case that you have got into production?

Mehul Nariyawala 7:20

That was a very deliberate choice.

Navneet Dalal 7:22

Yeah. Yeah. So the notion and idea is that, so we knew that robotics wave hasn’t really come, really. Like, you know, yes, there are robots, like, you know, iRobot was started in 2002, it’s the kind of the only consumer robotics company which will do this initial algorithm was, it’s kind of a random walk. So the robot is in a home, you start it, it will just go in a random direction, bump into an obstacle.

Mehul Nariyawala 7:46

It’s literally like you have a blindfold around your home and you fold your hand and keep bumping into things.

Navneet Dalal 7:51

Yeah. And when you bump into something, you turn in a random direction, go do it. So you can, in principle, clean an entire home if you don’t like, you know, get stuck or something. But you have to also need infinite amount of time to make sure, guarantee that you have done coverage all over the places. So it was, and that was kind of what could have been done with the technology back then because the algorithms were not there, the sensing suit was not cheap enough, like the computes were not really even feasible. Even if you have all of these things, you can’t really do all these computes on device in real time. And our work at Nest gave us this opportunity, like, OK, things are starting to fall in place, but it will still require years of work to develop the core stack in robotics because it’s not that today you can go download something on GitHub and say like, hey, the operating system for the robotics is working, perception stack is working, things are plugged in and you have a robot moving in, going from point A to point B seamlessly. And we realized that, OK, it will take time. And one of our core belief and thesis is that when building consumer hardware, you do not want to, with your first product, create a new market.

Siddhartha Ahluwalia 9:09

Why is that? Can you elaborate?

Navneet Dalal 9:11

Yeah. So let’s take some historical examples that would be perhaps a very good example. When Apple worked on the iPods back in 2001 and 2002, we were already using Walkmans. OK, Sony also had a network Walkman, which is kind of an iPod, but a small disk. And Apple said, oh, people are using Walkmans, let’s give them amazing Walkman experience. Right. And they built an iPod. When they did the iPhones, again, like, oh, people are using Nokia phones, BlackBerrys, etc. Let’s combine the browser capability, the mobile, the phone capability, the email capability and the music capability in one device. And that’s your phone now. Right. And similarly, if you look at like, you know, the watch, like, you know, people wear watches, it’s a digital watch.

Mehul Nariyawala 10:04

So even if you go for personal computers or PCs, you can say, oh, PC was in the market, but it wasn’t. Computers existed. There were a lot of professionals who had used computers, whether it was mainframes or the mini ones that they used to have. So this idea that computers could do things was already obvious. And there was a desire for a lot of these people to bring their computing home from universities or there. So there was a need that was obvious.

Navneet Dalal 10:29

And so again and again, even Tesla, like electric cars, like cars, people are already using cars. Tesla said that, hey, we’re going to give you electric cars with 200 miles range, super-fast acceleration, things like that. And so at least in the last 25 years, like, you know, which I would say as my professional journey or career, I haven’t seen a new hardware, like a business created with the first new product. Like, you know, for example, VR, AR is a new category and a lot of billions of dollars have been spent, but we still don’t know what to do with these devices. Right. And iPad was a new device and it wasn’t clear where to exactly fit the iPad and it took almost 10 years for various experimentations till it got figured out. It’s primarily for students taking notes in their schools and universities. So when doing a new product in a new market, it’s an extremely risky because you are taking a lot of market risk. And we were already taking computer vision, huge technical risk, etc. And then you add all of the market risk on top of it. And you have only one shot in the hardware business because by the time you build a product, you have already spent 200, 300 millions of dollars, like, you know, you have done all of that engineering work and then you get one shot and if the market doesn’t take off, what do you do?

Mehul Nariyawala 11:56

Isn’t it? I think that’s the key problem that in software, you can really go and iterate even within a startup and you can pivot a little bit. With hardware, you almost always get one shot because just the time it takes to iterate is so much faster that you probably do not have a time for whatever version two or product number two that you can think of. And so like the motto, one of the things we learned at YC was that make something people want. In hardware case, you almost always want to say make something people need. Okay, that’s one. And then second part is there were a couple of companies like you can say, okay, Fitbit created a new market. So there have been some successes. But what we also observed is that when you create a new market, incumbents come in really, really fast and then it’s really, really hard to create a mode or really compete in a long term. And then everything about floor cleaning category was so unsexy. And that’s why it was a deliberate choice that one market existed. I had never met and Amit had never met anyone who said Roomba changed my life. Yet category was growing 16% year over year, which means there was a more demand. Category net promoter score is literally negative one even today. So no one really likes their disk robots, no one really recommends them, yet people keep buying there. So need is intense. And it’s unsexy enough that Alphabet is not coming or Google’s not coming, Amazon’s not coming, Apple’s not coming. And if you graduate from Stanford with a PhD in computer vision, and tell your mom that I’m doing self-driving car, she’ll say, cool, my beta is amazing. If you tell her that, hey, I’m doing robotic vacuum, she’l probably say what the F is wrong with you. So that was the point that really, this is the place where we’ll be able to get a chance to build this foundation of indoor navigation and indoor mapping and indoor obstacle avoidance that you need for robots or true autonomy at length. And that was really the priority, that was the point of view. And to go back to the earlier self-driving car analogy, self-driving cars has two amazing advantages. One is that it has Google Maps. So it allows cars to know where the road is going. And second one is GPS. So it knows where that allows cars to know where it’s located. No matter how smart the car is, if it didn’t know these two things, it would be lost. We would be lost if we didn’t know where the road is going or where we are located. So in an indoor road, how does a robot know whether it’s on the right side of this table or the left side of this table? And that was the key piece of the puzzle that wasn’t solved yet. And that is required to do precise navigation and understanding.

Navneet Dalal 14:35

There is one more fundamental piece, if you take from a pure technology point of view. So we talked about that, hey, this is an existing market and customers want it. So if you build a product, you can sell to customers. From a pure technology point of view, you fast forward, let’s say Matic currently does floor sweeping and mopping. And you say like, okay, let’s say there is a robot which is in your home and it can do other things, maybe tidy up, maybe do dishwashing, maybe do laundry, etc. And ask what part of the current stack on Matic is not useful for that robot. So yes, there are skills which would be needed to be added extra. So this new robot has to have a super set of Matic skills, but there is no technology stack of Matic that you would say that, hey, I don’t need it for the next product. So the way we imagine it is that irrespective of what robotics come, they have to solve all the same kind of problems that we have gotten working on the device. And it’s not simply a function of demo. So in robotics, almost always, so in software, let’s say you can build a demo, you have done 80% of the work and you then spend 20% of the work productizing it.

Mehul Nariyawala 15:52

Great example is before it goes on, you have GPT-3 and then six months later, you have ChatGPT.

Navneet Dalal 15:59

Yeah. Yeah. And in robotics, it’s almost always the other way around, which is the demo is only about first 20% of the work and the rest of making sure it is working is the 80% of the work. And it takes quite a long amount of time to get to the perfection to make sure that robot can do the task in an autonomous fashion consistently.

Mehul Nariyawala 16:24

And then this is one other thing as well that with today’s AI, we collaborate, which is, you know, if you’re using coding AI, let’s say cloud code, if it does 90% of the job right, you’re mesmerized because it takes years for us to master that. Right. And we are happy to take it to the last 10%. But we don’t necessarily go to school to figure out how to navigate without bumping. We don’t go to school to figure out how to vacuum without breaking things or chewing wires. These are things we just know. So it’s a trivial task for us. These are the tasks we hate doing. So we don’t want to collaborate with a robot, we want to delegate. And the imagination we had is imagine, you know, we grew up in India as well. In India, most of us can afford domestic help even today, even if you’re a middle class family. If you had domestic help that came and only cleaned everything except one corner of the room, left dirty, you’d probably tell them, hey, make sure you clean that tomorrow. They’d leave that corner dirty twice, they’re fired. Because we don’t want to be doing that, we just want to give it up. So that was the point that look, as a homeowner and as a family man, I want to live in a perpetually clean home with perpetually clean floors. I don’t want to do it. I don’t want anyone in my family to do it ideally. But then there is a third requirement, which is I don’t even want to think about it. It should just work. And that just work part is actually quite hard.

Navneet Dalal 17:44

In fact, I would say this way that if we were to restart, let’s say, Matic today, we would go to solve the same problem.

Siddhartha Ahluwalia 17:53

Okay, so nobody else who has solved making a map of your home kind of problems.

Mehul Nariyawala 17:59

It’s a, I would even, so the analogy, and then I like to do it in a little bit of analogy way, because that’s how I think, you know, I joke that my knowledge on technical side is enough to be dangerous. So we are as a human, not more as a full blown humanoid. If you actually think about it, between zero to five year old human child, all they learn is their home and how to navigate and perceive it. And even they learn how to not, you know, fall, kill themselves by falling down the stairs or really going down the water or swimming pool or bumping into things or getting their fingers stubbed in joints in different places in furniture and stuff. So it’s really just about perceiving. So in the context of floor cleaning robot, we are really teaching how to perceive the world just as zero to five year old. Then five to 10 is when we finally start learning dexterity, when we first start learning language so we can add semantics. That’s when we start to understand the world. And then we do basic tasks like, you know, kids can all of a sudden put their shoes back in their place or organize maybe their bookcase or make their backpack. But it’s still basic tasks. We are not going to give this an eight year old where we usually do not give a plate full of grass or a tray full of grass. It’s only between 10 to 15 is when we start doing both long rise and planning as well as complex tasks of cooking and baking. So our thesis was always that it’s a three step process that let’s actually solve perception first to the nth level. And then actually, which is just simply that does robot know what home looks like? What are the items inside it? So today, Matic only knows as much as cats and dogs, which is it can navigate without bumping, but it still doesn’t. I still can’t tell it to go hang out by the couch in the living room. But can we add that information inside map and can we teach it to semantics as well? And if you get to that level, then once it has a semantic understanding, now you can start telling it to manipulate and move it. So that was the idea behind it. So it’s really that step process. And I think I mentioned earlier on that when we were talking of the camera that even if you have a humanoid, you still don’t want a humanoid to get tangled in your wires inside home. You still don’t want them to fall down the stairs. You still don’t want them to walk into food spills or spaghetti spills or even things along those lines. Every single thing that you want your humanoid to do, we are already solving. Matic is already doing that. And if you can manipulate, like you’ll see some of these demos where they manipulate, but they don’t usually walk as much. They don’t navigate in a complex environment around complex obstacles that you find in homes. So that still remains a challenge. And even for them, they will have to solve it. So we’re just doing bottoms up.

Siddhartha Ahluwalia 20:48

So how far do you think companies like FIGURE are working on full autonomous robots? How far do you think we are from fully robotic house help that can do everything?

Mehul Nariyawala 21:01

I think everyone will say that there are two parts of it. So one is if you go to academia and ask them about indoor mapping and navigation, they will tell you that it’s a solved problem. Now, if you are trying to publish a paper, it is a solved problem. But the way we thought about it is if it’s a solved problem, where are all the robots? Why isn’t our world swarming with them from airports to grocery stores, to hotels, to homes, to restaurants? We don’t have that. So clearly, this is not a solved problem. And we need number one. Number two, jumping directly to humanoid thing, the challenge there, as everyone knows, is the data problem. So even to build self-driving cars, Tesla has, I think there was some stat that they are collecting 500 years worth of driving data in a day. So if you just want to build a self-driving car, you need that much data. Humanoids and home environment are quite complex, quite unstructured, quite dynamic. So how much data do you need? So it’s a function of who solves that data problem and how do you get that data and how do you acquire that precision? I think there was a recent article that said that even if you see a 90% demo in a lab environment, the moment you go in a real world, it drops down to 60%. And closing that gap is really hard.

Siddhartha Ahluwalia 22:21

Yeah.

Navneet Dalal 22:21

So another analogy you can think of is that in case of a self-driving car, it has, in practice, three degrees of freedom. It needs to move in the two-dimensional floor, basically roads, et cetera, and direction it needs to go. And you have to avoid all the objects on the way. So you can be, in case of a car, a good three feet or four feet away or close to a meter away from the nearest obstacles, et cetera, and you can make it work. And we all know the amount of data and the effort it took to get Waymo and the Tesla FSD working. Now, within robotics and when it comes to humanoid kind of robotics that we are talking about, you first have the robot itself has multiple degrees of freedom. Even if we say it’s a robot which is just on the wheels, et cetera, moving three degrees of freedom, arm has seven, then the fingers have a few more. And then the object you are picking, if it is, let’s say, a water bottle, where do you want to pick it from? Is it on the top, at the bottom? If you squeeze too hard, maybe the water can come out. The object themselves have one degree of freedom. If there is another obstacle, let’s say maybe there’s a book or something, then grabbing an object and pushing it in this sense may not make sense. Grabbing and pulling it in this sense may make sense. So the world has very many degrees of freedom. And so I would easily say it’s 20 plus degrees of freedom, depending upon how people count. So it’s just going to take time. Now, is that time like, hey, five years? We don’t know. Maybe there is a new advent, new technology comes up, which makes it easier to learn faster with an existing data. Then, yeah, maybe. But it still requires solving, like, you know, currently, the problem solution still requires that, hey, first, you absolutely need data, the current way of doing things, tons and tons of data, in fact, bigger than internet scale data. And even if you don’t say, like, hey, you can do it faster, then it requires some advantage in the algorithm improvement and things like that. So it can happen. And the thing is that our take is that, hey, whatever we are doing currently is anyway needed to build those kinds of algorithms and data. So we are not necessarily losing an edge over any of the existing robots by going and building seligmatic. In fact, we are building muscles on how to sell to customers, how to make products cheap, how to make product reliable. And all these things, even after you have the technology working, they still have to solve. You still have to build, you build a robot, like, you know, you still have to solve, make it cheap, make it reliable, like, you know, have it feature rich, make the industrial design look nice, and then do customer service, brand, reverse logistics, supply chain, and scale it up. So these are all the things you still have to do. And I think we are just flexing those muscles also while building autonomous.

Mehul Nariyawala 25:25

The other way to think about this, and this is something we knew. So by the way, first robot vacuum was not Roomba. It was Electrolux. It came out in 2011. I mean, sorry, 2001. It was priced at $1,400 and it failed. It was packed with sensors and it failed. Then iRobot did this clever redesign of it and shipped it below $200. Now we had a chance to connect with Rodney Brooks, who was original algorithm person and one of the co-founders. And what we learned from him was that they were hellbent on keeping it below $200 because that’s the price point at which you don’t have to ask permission of your wife to make that purchase. So price point makes a big difference. Now, if you extend that, then you realize that there is literally zero ubiquitous consumer electronics device that’s priced higher than $2,000. Beyond $2,000, you pay for professional equipment. So either in a prosumer space or only thing consumers spend higher than that is cars. In cars, after 100 years of proven utility or vehicles, and let’s assume cars, at least in the United States, $10,000, $20,000 is the car price point. After 100 years of utility, still remains considered purchase. And there is this gigantic amount of infrastructure built from financial infrastructure to enable those purchases. Also, repair, oil change, tires, all kinds of infrastructure available for you to maintain this as well. So now if you take that analogy to say, okay, there is a $10,000 humanoid, great. Why do I buy it today? Do I buy it for laundry? Do I buy it for dishwasher? Do I buy it for babysitting? What is the lifetime? Do I expect it to work two years, five years, 10 years? What is the maintenance requirement? If I want to maintain it, how do I take it? Customer, is the maintenance person going to come to my home? Am I taking it? These are all the questions that customers care about beyond few early adopters. Every one of the customers care about these things. So there is a lot of that to be figured out. And then to go back to the earlier point I made, I think first DARPA self-driving car was 2006, if I remember correctly. So it’s been 20 years and we’re still working on self-driving cars. Now, a few things have changed. Computers have gotten better. Algorithms have gotten better. So let’s assume that the complexity of your humanoid, which is much more complex than self-driving car, it can be sort of shrunk or can be countered by speed and computes and improvement in algorithm. Even then, you’re probably looking at five years, maybe 10 years at least, maybe 20. So we think it’s far away. And there is an analogy for this. So history doesn’t repeat, but it definitely rhymes. And consumers have to be incrementally led. Their consumers typically don’t make a step if you think about how long it took us to adopt cars or adopt planes. It took a long, long time. So in the same way, in 1995, there was a company called General Magic started by these two guys named Bill Atkinson and Andy Hertzfeld, who were one of the most distinguished engineers in Apple Macintosh team. An entire ex-Apple Macintosh team tried to build iPhone. That was Tony Fidel, Nest co-founder’s first company. And they massively failed. And what we eventually got was Nokia feature phones. And then we got the PDAs. And then we got the BlackBerrys. And then we got the iPods. And then we were carrying three devices. And we’re like, I don’t want to carry three devices. So then need for iPhone was obvious. And iPhone came out and everyone was ready for it. On the flip side, today, people don’t even have a trust that the robot vacuum would do the job, right? They’ve been over-promised and under-delivered for 20 years. So our thesis has always been that next five years, seven years, we have a job to go earn trust of these customers, these homeowners to say, hey, I can sell you a robot and it’s going to do exactly what I think, what I tell, promise you that it will do as a product and earn that trust. And if you have three, four, five, let’s assume $1,000 robots, all of a sudden, if a humanoid comes and says, I can do all five things at $10,000, you might just consider it. So I think there is an element of building that need and creating that awareness inside customers that we are completely skipping over, not just the technology itself.

Siddhartha Ahluwalia 29:42

And when did you guys ship the first robot to the customer?

Mehul Nariyawala 29:46

November of 2024, Thanksgiving 2024.

Siddhartha Ahluwalia 29:50

And you have shipped 6,000 units till now?

Mehul Nariyawala 29:52

Correct.

Siddhartha Ahluwalia 29:56

And what’s the current pace of shipping per month?

Mehul Nariyawala 29:56

About 2,000 units.

Siddhartha Ahluwalia 29:57

And how are you generating demand for it?

Mehul Nariyawala 30:00

Luckily for us, at the moment, it’s all word of mouth. We haven’t done any marketing whatsoever, except the reviews and people on the internet, especially on Twitter and X, just a lot of people who have genuinely good amount of followers, just talking about how they love Matic, and that has been really the great way to sell it.

Navneet Dalal 30:22

And it’s only available in the US right now?

Mehul Nariyawala 30:24

That’s correct. That’s the deliberate choice as well.

Navneet Dalal 30:27

We haven’t expanded internationally. It’s not that we don’t want to do it. We absolutely want to ship to, we have so many customers pushing us or potential customers pushing us to ship in Europe, even in Asia, and even in India. It’s just that you, in a hardware business, you have to think of all these things we talked about, like logistics, supply chain, get it working, doing it properly. Repairability. So we are just focused on the US for at least this year. And then you would see that as we start scaling up, we would want to go out in other international markets.

Siddhartha Ahluwalia 31:04

And you got some amazing set of investors. How did you get them? From Sutter Hill to Collison Brothers.

Mehul Nariyawala 31:11

A lot of luck. I think being in Silicon Valley gives you an opportunity. So we’ve been here since 2005, both of us, so about 20 years, since 2006. And Collison Brothers, for example, when we were doing Flutter in 2011, we randomly ended up in their office. There was like, I think, MediaYC startup day. And one of the startups was Stripe. So we ended up there. And he, John Collison, was actually there. And we just ended up showing him a demo. And then he helped us get into YC. So that’s where the connection was formed. And at that time, we didn’t know Stripe was going to be as big. We thought we were doing something way, way cooler, honestly, than Stripe. But then very quickly, we realized that they were doing something useful. We were just doing something cool. And there is an academy there. But that’s where the relationship was formed. And we kept in touch with him. So in 2019, when we were raising our seed round, actually, I’m sorry, angel round, the 5 million one. That’s when we initially got them to invest in us. And they had already begun angel investing back then. And that’s really how we got it. So a lot of it is through connection. A lot of it is through just getting to know people. I think Navarro, we come the same way. It was pure dumb luck that his office, angel list was literally across Flutter’s office. So we got to know him that way as well. On San Francisco.

Siddhartha Ahluwalia 32:35

Your first company that saw large success like .com, you were among the early team members, right? Got acquired by Google. The second company that you were founder of, Flutter, again, got acquired by Google. So I see a common pattern there.

Mehul Nariyawala 32:53

Back to mothership. No, I think I don’t need to jump in. But I think at that point in time, between 2005 to, I want to say all the way till 2014, Google was really the only company really pushing the envelope on AI and envelope on machine learning. They were really ahead of the curve and looking into it. When we were doing Flutter, unlike today, where you just expect AI and everyone expected to work. Back in 2011, 12, no one believed machine learning worked. No need that actually. So the story is that like .com came after, I mean, sorry, Google came after like .com. Acquisition was about to happen. I stayed through the acquisition and joined Google for a bit. Navneet actually left way before that. Yeah. And started working on Flutter.

Siddhartha Ahluwalia 33:47

Okay.

Navneet Dalal 33:48

And the whole look, so my background is in computer vision. And the whole idea was that I really believe that this is a space and domain computer vision, machine learning, which has tremendous potential. And the idea was to like, hey, if you start the company on your own, you’re not going to give midway, but you’re going to see it through in terms of technology, working and shipping it to customers. And that’s what we did at Flutter and got it working. It was the number one app in 72 countries for a good six months of 2012. Like, you know, Apple even ranked it in their app store as one of the best apps of the year. And we got about like, you know, million downloads on the desktop app, which is almost about like you can say a factor of 10X when compared to the mobile apps. And got about like, you know, close to 77 million gestures in a very short amount of time. But entirely honestly, outside of Google research, nobody really practically believed that machine learning and AI can work. And they were kind of at the forefront. They saw the potential in the technology. We were state of art in terms of gesture recognition and object detection back then. Even after the launch of AlexNet in 2012. And what we built as a technology was eventually superseded in 2014 and 2015 with the masks, CNNs, etc. So I think they saw the potential of what we were doing. And yeah, so it just ended up kind of, you can say, events just lining up properly. Right place at the right time.

Mehul Nariyawala 35:33

But this was a different world to give a little bit of credit to venture capitalists in Silicon Valley. Machine learning was popular in the 80s. Never worked out. Machine learning was also popular in early 2000s. So never worked out. So it wasn’t necessarily clear that what changed now that will make it work. And reality was that we were getting data, we were getting internet and iPhone cameras were solving a bunch of problems. But it wasn’t obvious to you. And then when Apple picked us as one of the best of 2012, they actually approached us initially for acquisition. And then Google got interested. And by that time, we knew that this was really, really good technology, really, really good UX, but no one wakes up in the morning and says to them, I’m going to buy gestures. So there was no business model. There was no way for us to-

Siddhartha Ahluwalia 36:16

What were you solving for consumers?

Navneet Dalal 36:19

So what we were solving for consumer is that this is also the time when a lot of folks were moving to their personal devices for consumption of media, like laptops, iPads just came out and people were switching it for watching videos. And our whole point was that can we move and get working a technology where you can control your devices from a distance? So if you’re watching a YouTube or a Netflix, et cetera, you can shush with the mute. You can give a thumbs up if you like something. Okay, you can just raise your hand, pause the video, take a call from your friend. Once done, you again raise your hand, start playing the video again. So it was our view of looking at like how we can bridge the… If machine learning truly works, the interactions would be multi-modal. There would be audio, there would be natural interactions and the video would all be merged together. And so we got the technology working, but we also knew that this is eventually would be better solved in a setup box under your TV so that if let’s say Super Bowl is happening and like someone is calling or something, you can raise your hand, pause an online streaming media, just take a call and then raise your hand again. And we build amazing technology, but we were way ahead in terms of an adoption. The setup boxes, the camera still haven’t happened even after 12 years in some sense. And we realized that we build amazing technology, but not solve the business problem.

Mehul Nariyawala 37:55

Yeah, there was a period in 2011, around 10 when Kinect launched. So Kinect still probably remains the fastest selling consumer electronics device. In the first 60 days, they sold 8 million units.

Siddhartha Ahluwalia 38:08

And what was the device XX?

Mehul Nariyawala 38:10

And it was just device to create a gesture detection, right? So if you’re a Microsoft Kinect, it came with their gaming console. Xbox?

Navneet Dalal 38:18

Xbox.

Mehul Nariyawala 38:19

And you could use Kinect as a gesture detection. But what they did was they turned your hand finger into a mouse. This is typically how gestures are done, right? And we had this insight. There were two insights actually. Navneet with his computer vision background was always saying that, look, we shouldn’t need time of flight sensors. We should be able to just do things with the RGB cameras, which is instead of when you add a time of flight sensors, what you’re doing is trying to build better eyes. And Navneet’s point of view was, let’s build better brains, better algorithms. And then second part was as Navneet alluded earlier that whenever people do this, turn your finger into a mouse. We just felt like it was really cumbersome that if you have a TV and you want to mute it, raising your hand up in the air, trying to find that mouse pointer and then clicking a mute button is really cumbersome. So why can’t we just say, shush? So the idea was as computers get smarter, they should understand us, not the other way around. So we were relatively ahead of that curve in a technology curve. There was a period, if you remember, where Samsung TVs and other TVs used to come with cameras as well. So there was this idea that maybe gestures will be ubiquitous, but then it just changed. Mobile took over.

Siddhartha Ahluwalia 39:29

Got it. And if you can share roughly how big was the acquisition during that time?

Mehul Nariyawala 39:35

Yeah, so we raised about 1.5 million there. So it was very early on back then. There were no pre-sale funds. There were no neon funds that we would raise from. So it was actually a very different world. And then we were acquired for about 35.

Siddhartha Ahluwalia 39:48

Okay, amazing. And were Collison Brothers and Naval Ravikant investors in Flutter also?

Mehul Nariyawala 39:53

No, no. Flutter was very different. Flutter, we had good investors back then too. But honest answer is Flutter was very different. Meaning that if you kind of roll back time to 2011, I mentioned there were no angel investing. Maybe there were 50 angel investors. There weren’t that many.

Siddhartha Ahluwalia 40:11

Because people hadn’t made their money through angel investing. I think it’s only through Twitter, Uber, and all these kind of…

Mehul Nariyawala 40:17

And they didn’t have a deal access. So angel investor investment also wasn’t a thing because there was no angel list or there were no platforms or this idea that you can invest in an angel wasn’t obvious to a lot of entrepreneurs who had made money as well, right? I mean, there were angel investors for Google things as well. But this risk appetite hadn’t sort of come in as well. There weren’t as many pre-seed or seed funds as well. So we had to go to big companies and we were actually really, really struggling to raise for a long time for Flutter.

Siddhartha Ahluwalia 40:51

Okay.

Mehul Nariyawala 40:52

It was really hard. And Navneet had done a phenomenal job of actually building a demo before I even joined him. So there was an amazing demo, but even then we were struggling. And what happened is sometimes you get lucky. So my first job was at Salesforce.com. One of the colleagues from Salesforce.com had gone and started this company called Okta. Okta. Yeah, and Frederick Christ, right? So he’s a very popular value-added loan.

And Andreessen Horowitz had just started and they had funded him. So I reached out to him and he made an intro to Mark and Mark Andreessen at that time had a thesis that gesture would be ubiquitous, similar to us. So we were just at the right place at the right time.

And then once we got them to come in and they didn’t really put in a lot of money, it was just $100,000.

Siddhartha Ahluwalia 41:40

That’s it.

Mehul Nariyawala 41:40

That’s it. And they don’t do a lot of seed round.

Navneet Dalal 41:43

And out of their seed round, like now they have this seed round, okay.

Mehul Nariyawala 41:46

Seed fund, yeah. So it was just a small amount, but the fact that Andreessen invested just, yeah. We were, as I said, we were struggling to raise funding and all of a sudden Mark and Andreessen Horowitz investor. And I think a week later we had a million and a half raised.

Siddhartha Ahluwalia 42:01

So before Sutter Hill came in, in the 60 million round that you had.

Mehul Nariyawala 42:04

Formatic, yeah.

Siddhartha Ahluwalia 42:05

Formatic, right. How was your journey of fundraising formatic?

Mehul Nariyawala 42:10

So formatic, okay. So, you know, both Navneet and I talk about this. We’re not necessarily the best fundraisers in the world. We have not been able to ever raise just based on a deck. For us, funding has always been an output, not an input. So first time we built this amazing demo as Navneet was telling you in 2019 and raised our angel round and we could show everything that we are doing in magic today, we could show it back.

Siddhartha Ahluwalia 42:34

There was no deck, there was just a demo.

Mehul Nariyawala 42:36

There was a deck, but demo was the key piece of the puzzle. That it’s not just this vision that, hey, we have proof of concept. So 2019 was a proof of concept. By 2021, we had working prototype. So it was 90% there, I want to say. But in robotics, 90 to 99 is 2x the work. Maybe even 99.9 is 3x the work. So we had that demo and we had the white version. In 2021, Matic looked exactly the way it looks today. So we had industrial design. A lot of these things figured out and we built this demo just a 30 second. Wait, it was maybe a 90 second video. And showed it to a bunch of our angel investors. And luckily for us, John and Patrick Collison and then Nat Friedman and Daniel Gross, they were just really interested in investing. So they just came in and took the whole round.

Siddhartha Ahluwalia 43:26

Wow. And that was the 5 million.

Mehul Nariyawala 43:28

That was, no. So the first 2019 was 5 million. 2021 was 23 and a half million dollars.

Siddhartha Ahluwalia 43:35

And all by angels.

Mehul Nariyawala 43:36

All by angels.

Siddhartha Ahluwalia 43:37

And then next?

Mehul Nariyawala 43:39

Next was the bridge round, yeah.

Navneet Dalal 43:42

Yeah. So next, basically, there was a time between 2023 and 2024. Like 2024, I would say, when we struggled again to raise. And in this case, it’s like, hey, we were on a different chipset before switching to NVIDIA. And we realized that the software for the chips was not really good. And we are getting bottlenecked. Even if we try to ship the product, we will die very, very soon because you can’t iterate fast enough from a software and algorithm point of view because the compiler for the chip for the NN accelerators was very bad. And we made a decision to switch over to NVIDIA. So there was a time when the whole product is kind of ready. And in 2023, we said, we’re going to redo it. So we changed our SOM. We changed all of the memory layout, et cetera, because we changed the SOM. We changed the cameras. We had to redo all of the platform work, the firmware work. We had to how the stuff is laid out in the memory that has to be redone because now we have a different system which has a different kind of APIs, different preferences on how the stuff should run on the memory. We do entirety of the EE. You can say, take the guts out and put the new guts back in entirety of the platform software, the compilation of the code, et cetera, put the new gut back in, got on it done in like kind of a summer 2024. And then we knew still then like, look, in robotics, where we are, when you’re so close to the product, you got to ship the product and get it working. We focused on just shipping the product at that stage. And this is also a time, luckily for us to some extent, to Mehul and I, we had a capacity to continue to putting our own money in.

Siddhartha Ahluwalia 45:36

So that’s when you went from one and a half million to 35 million?

Navneet Dalal 45:39

Well, we did 5 million in the previous round also. So we did one and a half first, then we did 5 million with John, Patrick, Matt, and Daniel came in of our own money. Again, like skin in the game thing. And then we’re like, okay, this is, we’re going to have to, we have to see it through till we ship the product. So we kept putting money in till we ship the product. And even after that, because just on the product, you ship the product every next day, everything is going to work. You have bugs, you have issues, you have features that you have to still ship. And we kept pushing all the way on that front. And what happens is that in the valley is like, you know, people bought the product, they use the product, and slowly and slowly started taking off.

Mehul Nariyawala 46:22

And that’s, yeah. And the way to think about this is, this is not, the struggle wasn’t just like, you know, it’s not investors fault or anything. We just took a longer to ship. We, ourselves did not think we were going to take this ship. Like the joke inside Matic is that, oh, we were only off by one digit because we’re going to ship in 2020. And we were already initially, at least in 2017, we thought that we’ll ship in 2020 and we were shipping in 2024. So it just took way longer. In hindsight, looking at everything we’ve done, like kind of go back in time. And aside from this nine months, where we sort of made a mistake of picking Ember Airlines instead of NVIDIA and had to switch. I can’t find any time where I could shrink our timeline. So it just, robotics just takes a long time. And we understood that. Like I said, that if hardware is 10X harder than robotics, I’m sorry, software, robotics is 100X harder than software because you’re doing hardware, software, platform, algorithms, everything.

Navneet Dalal 47:17

And autonomy.

Mehul Nariyawala 47:18

And autonomy. So full stack takes a long, long time. So it just took longer than expected. And that’s where we had to put in our own money. And we had to ship. And that was really it that, hey, can you ship? And everyone was asking, can you ship as well? And luckily for us, because of the patient capital, we knew that zero to one was going too hard. It turned out to be even harder than that. So we lucked out. And luckily we had an opportunity to do it. But we were seeing good signs. So you mentioned that during that round, Ashish came in, right? So one of the first person to you get our NVIDIA-based robot was Ashish. He had gotten one of the very, very early units. He used it and he saw the potential. So there were always glimpse of that, hey, we are heading in the right direction, that customers are loving it. And we had actually built about 50 umbrella-based robots and put it in customers’ home. And they barely worked in any sort of low lighting. They were just okay. And if it was bright light like here, it would work fine. But it was even slightly, even like a dust kind of lighting, it wouldn’t work at all. And there were a set of people who kept using even that kind of robot for six months, eight months. So we knew that we were heading in the right direction. If we could just fix it, there is a genuine utility that people want to have.

Navneet Dalal 48:32

So before we shipped, just after Thanksgiving 2024, we had internally used the robot for more than two years. We were iterating on the product all along. It’s just the stack took a long time to build. Everything, just making sure there are no bugs, the crashes, et cetera, build tools around them. And then of course, redoing it. But yeah, it just took time to make sure it matures up. So one of the downside, which is like when you are coming in an existing market, we talked about existing market, new markets, right? The downside of an existing market is that you have to come with a minimum lovable product, which is you have to mature your product to a certain degree because customers have expectations after having used existing products. Like a very crude example would be, if Tesla has to launch Model S in 2012, they didn’t put an electric engine on four cart wheels and ship like how initial cars were shipped in 1880s and 1890s. With a crank or no roof. Like it’s like a horse cart, but you add a motor engine. They had to make sure the cart looks extremely beautiful. Industrial design is top-notch. Everyone just looks at it. Whenever it crosses three, it has 200 miles of range, like the acceleration, which is through the roof, the inside looks modern, has the safest safety rating of a car and the crash rating of a car. So the seatbelt works, the paint is looking nice. The basic things are there. So essentially, the downside of an existing market is, and that’s why perhaps very many people try to avoid it because it just takes longer time to mature a product.

Mehul Nariyawala 50:19

That’s correct. Yeah. So that’s what we did. And then really the last round came together again. We shipped a lot of unbeknownst to us. Some people, one of the managing directors and partners at Sutter Hill Ventures named Pete Schlamm got the robot and he fell in love with it. And he started talking about it internally.

Siddhartha Ahluwalia 50:40

It was an inbound from Sutter Hill?

Mehul Nariyawala 50:42

No. Yeah. So not both. So then I also knew another managing director in Sampler and we got him a robot as well and he liked it. And then they just started talking about that, hey, if someone is interested, we’ll let you know. And it turns out Rick Miller, who ended up leading the round and he’s one of the managing directors there as well, was interested in Matic. He came in and spent about three and a half, four hours just walking around, looking at everything we did. We didn’t have any deck or anything, but just based on the product, now we were making progress. It sort of came together.

Siddhartha Ahluwalia 51:12

And how fast was this process of getting the Matic to the first managing director and then plotting the round?

Mehul Nariyawala 51:22

I don’t know when Peach Slam got it, but from the moment we came to, I guess, close was maybe four weeks, three weeks, four weeks. Yeah. Very fast. I think we knew Sutter Hill’s reputation. We knew that those guys are like, the best way to describe Sutter Hill Ventures. And I think I had mentioned that they are one of the first and the original evergreen fund in Silicon Valley. They became evergreen fund in 1978. And since 1963, they’ve returned 35% IRR every year. So that sort of track record is just mind boggling. And then they incubated Pure Storage, they incubated Snowflake. I mean, they own 23 to 25% of Snowflake, I think, when it went public. So it’s a very well-reputed fund, very well-respected guy. And because they mostly incubate, they are sort of builders disguised as investors. So we knew their reputation and their thesis is exactly like ours, where they tend to take technical risk, not market risk. And that’s precisely what we had done. So it just worked out.

Siddhartha Ahluwalia 52:25

But let’s say iRobot filing for bankruptcy, did it change any of your plans?

Mehul Nariyawala 52:32

It doesn’t change the plans, it increased the demand, number one. And then it sort of increased interest in us as well, because for all practical purposes, we are the only consumer robotics company that is shipping in the United States. And ironically, the largest consumer robotics company in the United States, even at this scale. So there isn’t anything else out there. And in fact, I would actually venture, this is one of the other pieces of the puzzle that a lot of people miss about robotics. There is only the largest category in robotics is still just floor cleaning robots, robot vacuums worldwide. Any guesses, I’m gonna ask you, any guesses how many Roombas iRobot has sold in their entire lifetime?

Siddhartha Ahluwalia 53:16

Maybe 10 million?

Mehul Nariyawala 53:17

55 million. Okay, 20 million, 21 million robots were sold in 2024. I don’t have a 2025 number yet, but 21 million robot vacuums were sold worldwide in 2024. So sheer scale there is enormous. After robot vacuums, the next highest category is Amazon Kiva Robotics, which is really just warehouse robots that can take pallets also very much like Roombas, flat robots, right? Those are 1 million. Okay, then if you kind of go down all the picks and sort of packaging robots or any sort of humanoids or anything like that, at least in US, none of those robotics companies have scaled maybe hundreds or maybe a few thousands here and there. I think Boston Dynamics had shipped about three to 5,000 robots in their entire lifetime, if I’m not mistaken. So the scale for robotics is not there anywhere except this category.

Siddhartha Ahluwalia 54:19

Got it, and when you both combined together to put 35 million in the company, why your family is worried that like a large part of network?

Mehul Nariyawala 54:29

Well, we’re both a little crazy. Our wife thinks we’re a little crazy and wives also has given up on us but no.

Navneet Dalal 54:36

I think the honest way of saying it is that, look, just like how each company is just not us, like Matic is not us. They have very many capable people which are working at Matic to help us succeed, okay? And we rely day in, day out on them to take the right decisions, working through things. Similarly, I think perhaps we both got lucky because in the sense that our wives were very, they knew this won’t be an easy journey, but they were very supportive of us doing the effort, right, of us saying that, hey, you have worked on it for years. You have, if there is a clarity of the vision, et cetera, and kept supporting us throughout this endeavor. Would we say that, hey, it was the easy journey? No, but at the same time, I think honestly, we were not here without their support and kind of unwavering support throughout this time.

Mehul Nariyawala 55:30

If you kind of put the investor head on from our perspective, we had the, if you have money, you’re going to invest somewhere, whether you put in a bank account, maybe in stock somewhere. Well, it turns out we have the most information about Matic, right, even our wives were using that robot. Like I would, even in that, before we ever shipped, my wife was already relying on it. And every time if I tested it and put it on some beta release where it stopped working, she would get mad at me. So when she saw that there is a utility that we are actually heading in the right direction, so that conviction was there. We had the most amount of information. So it was very much this, you know, obviously we want to have a skin in the game, but we also knew that we were heading in the right direction, that this is some, what we’re building is correct. And at some point you have to put money where your mouth is. So we did. Yeah.

Navneet Dalal 56:18

And look, I want to put some emphasis here in the same thing. It’s not that we genuinely like, oh, every single founder has to put money in the company. It’s not like that. I think it’s just that we did certain things because we felt like this, it will take time to mature Matic and it requires this long-term viewpoint. And we genuinely believe that we need to have a skin in the game. But as our capital requirements goes up, it’s not that we have like, you know, billions of dollars in the bank that we can continue putting money in, right? So it’s just, we internally felt this is the right thing for us to do at that stage in the company. And that’s what we did.

Mehul Nariyawala 57:01

And also we’ve still raised relatively much lower amount of money than many of the humanoid companies or many of the human home robotics companies that have been funded lately.

Navneet Dalal 57:10

Or I would say hardware.

Mehul Nariyawala 57:11

Or even hardware companies. Like I think Humane had raised a lot more.

Navneet Dalal 57:15

400, 500, like, yeah.

Mehul Nariyawala 57:17

Yeah. So one of the things that does happen when you put your own money is capital efficiency becomes a priority. And that does force you to do, hopefully make good decisions. I think we generally say that constraints drives innovation, right? So this is also one of the constraints.

Siddhartha Ahluwalia 57:32

And let’s say outside of the Matic equity that you hold, the money that you put in, how much percentage of your net worth had gone into Matic?

Navneet Dalal 57:41

Oh, I would say it’s kind of pretty high. Like I would say, yeah, 70%.

Mehul Nariyawala 57:48

There is no plan B. There is only plan A.

Siddhartha Ahluwalia 57:51

Except the home and Matic.

Mehul Nariyawala 57:53

That’s really it. Except for the home and Matic. Yeah. It’s actually, you know, so about eight months ago, my dad passed away, but he’s a businessman. And throughout the journey, he kept saying that if you had to sell your home, sell your home. What you have to do, keep Matic alive. And you have to keep going Matic. Which is when you have a conviction, go for it. Because homes, you can buy again. Companies, you can do it again. So there was that, you know, I’m very lucky to have him in my life. But there was this mentality that, hey, we never had to get to that stage where we had to sell homes. But that was the mentality that there are, in any business, you’re going to have a near-death situation. Any business journey, adventure journey, you’re going to have a tough time. And how do you survive? Which is, how do you find Nemo? You just keep swimming. How do you succeed? You refuse to fail. I think there’s a lot of those cliches are a little bit true in some ways. And we just knew that we had to keep going.

Siddhartha Ahluwalia 58:46

So today in a $1,200 product, and this is without any subscription, right? It’s just one time, $1,200. What is your cost that you have to incur? In terms of parts, majorly?

Mehul Nariyawala 58:59

Yeah, so we would be gross margin positive if it wasn’t for tariffs. So we’ve been pretty deliberate about doing that. And I think if we keep doing what we are doing, we should be potentially cashflow positive very, very soon. So there is a path to get there. And a lot of that involves how we do it. So one of the choices we made, for example, is we sell it, you mentioned earlier, right? Like we sell it directly from our website. And we just do built to order model. That helps us with working capital, number one. So turnover in Indian parlance. So we have neutral to reverse working capital if we can keep doing that. And then second thing is if you go through any retailers, you do give 30 to 40% of your margins of it, right? Now, one of the most underrated innovation of Tesla is actually the direct sales model. They don’t have a dealership. And that saves about 30%. And if you look at electric cars as an industry, no one except Tesla makes money on electric cars. And Tesla’s margin is about 17%. So, if Tesla was going through dealership, even they wouldn’t make money. So, you sort of have to look at what has succeeded, what has worked out, and you have to replicate that model. So, we knew that build to order is the only way to go to it.

Siddhartha Ahluwalia 1:00:19

And how did you price it at $1,200?

Mehul Nariyawala 1:00:23

Both. We did some research. I mean, initially, okay, I’ll take a step back. Marketing, pricing, all that stuff we expect to get right on day one. Learning is that every product is different. Every category is different. Most of the pricing theory that you may learn in business school is sort of narrative fallacy. It’s a reverse to work there. Ultimately, you have to go out and test it out. There is a range. We couldn’t have priced it at $2,500. So, obviously, that’s the wrong move. But we did have this ambition, and at least I would say this food I would put on me. I had this dream that we would be able to do robotics as a service model. So, we launched that way in 2014. I was wondering, you may have done it.

Navneet Dalal 1:01:05

Yeah.

Mehul Nariyawala 1:01:06

I was hellbent on trying to do it. And this was my wishful thinking, if I were to say it. But we launched that way. And that was like an organ rejection.

Siddhartha Ahluwalia 1:01:14

Why?

Mehul Nariyawala 1:01:15

I think there is just one, at least in America, people are just tired of subscription. I don’t know about the Indian market.

Siddhartha Ahluwalia 1:01:22

They don’t want to pay $100 a month.

Mehul Nariyawala 1:01:24

It’s just another thing I have to pay monthly service for. And that was one. And then most of the people, this idea that you, instead of buying a robot, you subscribe to it. Most of the people came to our website and said, wait, why subscription?

Siddhartha Ahluwalia 1:01:37

We don’t feel ownership in that regard.

Mehul Nariyawala 1:01:39

Yeah. And they weren’t asking why Matic. They were saying why subscription? That’s the wrong question to answer, number one. And number two, I think at least for homes, there is this innate desire to own. And most people in the world don’t lease their cars, don’t lease their scooters.

Siddhartha Ahluwalia 1:01:54

But Google has proved that subscription model works in healthcare.

Mehul Nariyawala 1:01:58

It does. But those are very different things than owning for your home or personal devices. Like we don’t like renting our phones. We don’t like renting our cars. We don’t like renting our scooters. Generally, people do not like renting. So there is this innate psychological desire to do it. So part of the thing that we kept hearing from customers is even if I start initially as a subscription, why can’t I just own it after two years or three years? Why isn’t it mine? And we know this now a little bit more than even before, because now that we are inside home, we just see the sheer amount of connection that people are making with it. So Lenny Richesky just tweeted that his son always writes full names from his family and Matic robots is one of them.

Siddhartha Ahluwalia 1:02:44

XXX Lenny?

Mehul Nariyawala 1:02:45

Lenny’s son. Yeah. So it’s like my kid, he’s just posted a picture saying the MADEC robots is always part of our family. And is he an investor also? He became an investor after he bought the robot or after he got the robot. So he came in last round. So that’s where we realized. So there is just this connection that people want to make with a robot and people want it to become part of the family. And if you’re renting things, it’s not really part of the family.

Navneet Dalal 1:03:13

The point is that we took technology risk, we took product risk. Product risk in the sense it’s a white robot, bigger, taller, etc., rectangular shape. So there are a lot of modes which we broke with the product. We went in the existing market, so you can say we didn’t take a market risk. But with the subscription, now you added one more thing. So instead of the questions coming up, like why all these other things, like why cameras, why this kind of a size, film factor, etc., the first question becomes a pricing question. And I think that is not really the good first thing to do with the new product. Would there perhaps be a time and opportunity in two years from now when the product is scaling well, people understand it, they have gone through the word of mouth at mass, etc., they would say that, hey, I would rather think of it as a subscription. Maybe we would want to explore it again at that stage. But that didn’t work out. Then we changed the pricing, and we struggled how to price it. We were like, hey, is it a 1500, 1600? We also wanted to make sure we don’t… We were still trying to figure out what the product would… What would be our bond cost? What would be our actual cost of the cost of the goods sold? So we had an early estimate. So we’re like, okay, we don’t want to be net negative. We went with that thing. And then as we mature, continue maturing it up, we’re like, okay, we think we can… We know where the cost of goods sold would land up, etc. And that’s when we put the price up of about like 995 because the features were missing as a feature. Introductory price, like when the features matured up a bit, then we increased the price to 1095. And then when we’re like, now we have to do more work next year to do more pricing. So then we increase the price again.

Mehul Nariyawala 1:05:08

And tariffs and everything that plays a role. But I’ll go back to this point of view. I think that we are going in an existing market, an existing market, whether it was vacuums or robot vacuums, you were just used to owning it. So changing that behavior actually is really, really hard. And I don’t… There are not that many items that I can think of aside from maybe set-top boxes or cable boxes that we actually rent. So there is just this desire for people to own. It’s a bit more innate for whatever reason. I think especially in connection to homes.

Siddhartha Ahluwalia 1:05:44

Yeah. And any plans of launching in India, like a person can take this product to India and…

Mehul Nariyawala 1:05:49

You can.

Navneet Dalal 1:05:50

You can take the product and it will work.

Siddhartha Ahluwalia 1:05:52

So how do you manage the electricity and…

Mehul Nariyawala 1:05:55

It’s already dual core. So our dock is already dual charge capable.

Siddhartha Ahluwalia 1:06:01

You just have to put the plug like…

Mehul Nariyawala 1:06:03

You just have a simple outlet, simple adapter.

Siddhartha Ahluwalia 1:06:06

This is so cool. I’m glad we did this podcast. Thank you again.

Mehul Nariyawala 1:06:10

You should come by to our office if you’re…

Navneet Dalal 1:06:12

If you’re around, you would actually… The hardware, what it means to actually get the hardware done, you would see it.