Moving 9,000 Pounds to Shift 3,000: Why Tesla Vets Built Better

"We're moving 9,000 pounds to move another 3,000 pounds. It just makes no sense." This brutal assessment of modern warehouse operations came from Ahmed Betel Mal, co-founder of Mytra, as he described the absurd inefficiency plaguing a $4 trillion global logistics industry.

Mytra is a 2-year-old robotics company that emerged from the manufacturing trenches of Tesla and Rivian. Co-founded by former Tesla veterans Chris Walty (CEO) and Ahmed Betel Mal (CTO), the South San Francisco-based startup is tackling what they call the "red blood cell of global GDP" – pallet movement – with a radically different approach that could reshape how the world moves physical goods.

In this interview, Walty and Betel Mal reveal why they walked away from Tesla's humanoid robot project, how a beach vacation in Hawaii led to Mytra's founding, and their contrarian bet that the future of robotics isn't about building more complex machines – it's about radically simplifying them.

Chris Walty: My name's Chris Walty. I'm the CEO and co-founder of Mytra. We're a 2-year-old company based out of South San Francisco. A little bit about my background before starting Mytra, I was at Tesla for 7.5 years. Before Tesla, I had various positions in renewable energy, and consulting. I was an electrical engineer undergrad, but at Tesla, I joined to lead product and hardware for the supercharger team and I launched various products like paid supercharging.

Ahmed Betel Mal: My name is Ahmed Betel Mal. I am the CTO and co-founder at Mytra. Mytra is building robots for warehouse material flow, trying to solve a fundamental problem with material flow in warehousing, logistics and manufacturing as well. So I joined Tesla in 2014. I think this is after the dual motor Model S was launched or was announced. You know, there's a lot of skepticism. Are electric vehicles going to be successful? Is fully battery electric vehicle or plug-in hybrid. Tesla versus Fisker, a lot of uncertainty in the market then, but I joined Tesla to solve a very clear problem.

Ahmed Betel Mal: I was working on more traditional wheeled automation and of course, a lot of the fixed infrastructure projects that we had at Tesla beforehand, and then this challenge of let's build a humanoid robot was given to our team because we had an integrated robotics team, but the challenges of a humanoid are very different than building a wheeled robot on the floor. Legged locomotion, grasping and the manipulation, the kinds of decisions that this robot has to process, perception systems are wildly different.

And so first, Tesla had precisely 0 expertise in humanoid robotics at the time, so I had to go and figure out who the experts were, meet with them, try to establish a recruiting pipeline, try to find the internal experts within Tesla who were willing and able and also had the foundational skill sets to be able to solve this problem. I had to give myself a two-month crash course in humanoid robotics. So that was really an interesting time.

But you know, I think one of the things that Tesla teaches you is to be very resourceful, that wasn't the first time I've been asked to solve a problem, which I had no really deep or meaningful background in, but you structure the problem, you go find the information you need, hop on a plane to meet with the experts as you need, you do what it takes to figure it out. Elon laid a mandate out that we needed to get some robot walking in 3 months.

Ahmed Betel Mal: I think as a whole though, all robots are not created equal, all robot challenges are not equal. Anytime you're talking about legged locomotion or just advanced grasping, the perception that the type of data and the consistency of data, the frequency of the refresh rates, like, I'm not just looking at a hand, like I need to understand the forces, and there's literally infinite ways I could grab something. The difficulty of repeating that and making that some sort of industrial process is many orders of magnitude more difficult than just driving a bot around the floor of a factory.

And if we think about how difficult it has been to do full self-driving cars or autonomous vehicles, you could argue that that problem is not a solved problem yet because we don't have ubiquitous cars driving highways and surface streets yet, right? You do have companies that are driving, like you can walk outside of this building, you will see an unmanned car drive by this facility probably in 30 minutes, but it's not ubiquitous, right? It hasn't been adopted.

Building a humanoid to operate at full scale in a factory or a home at the comparable productivity of a human is a very, very challenging problem to solve. I'm confident it will be solved. I think the timing is everything, right? Is this a 1-year problem? Is this a 5-year? Is this a 10-year? Is this a 30-year problem? Unclear.

Ahmed Betel Mal: After Tesla, I tried to focus a little bit away from manufacturing. Let me think a little bit, do something else, derivatives trading and try to learn more about AI and financial systems, but the bug of manufacturing, working on hardware got me again. And the things I did at Tesla were very applicable to Rivian, and I thought I really wanted to help one more US auto manufacturer get off the ground. I was really excited about Rivian trying to help them out. So I went and spent time at Rivian, helping them scale their manufacturing systems as well.

After that, I was at a beach in Hawaii. I still have the video while I'm recording the beach, and I was like, wonderful, this is great. I'm done. I don't want to do anything else. I've solved every problem I want to solve. As I'm recording that video, I get a call from Walty, and he's asking me about, hey, do you remember the problems we ran into at Tesla during manufacturing? Let's really solve it.

At the beginning, I was like, I don't want to do this. I don't want to do manufacturing again. I want to stay at the beach. But the more we talked about how we would solve the problem, like, go back to the fundamentals. Let's not just build another automation company. Let's do something completely different. The more we talked about it, the more I got excited because it wasn't just another implementation, it's a complete rethink, and that was very exciting. So I said, OK, yes, let's start this company. Let's find the best people that we can find from our network and tackle this problem.

Chris Walty: So Mytra was founded because Ahmed and I, having seen a lot of these different types of challenges at Tesla and Rivian, wanted to find a problem that we could solve that we are uniquely positioned, knowledgeable to tackle, but also was just simply not being solved, right? Think of the lowest hanging fruit in your tree. And again, material flow is most of the work across industry, just moving things around. Yet it's clearly unsolved. I can go out and buy an industrial automation system to do almost anything I need. The problem is, is it cost effective? Is it simple? Is it extensible? Is it flexible?

And we said, if we can design something that's just far simpler, iPhone's a great example. Tesla's, there's lots of great examples in history where to follow a new S curve, S curve of adoption, right? A new technology, it starts off slowly, and then there's some acceleration, then it kind of tapers off. But to truly continue linear adoption and improvement of technologies, it's comprised of a whole bunch of small S curves. Right? We needed to create that new S curve for industrial automation. There needed to be a new paradigm. And to do that, again, when you look across history, it's usually the simpler thing wins.

Ahmed Betel Mal: Fundamentally, the material movement for warehouses and manufacturing happens on pallets. Pallets, in my view, are the red blood cell of the global GDP. Everything that you see around you has been on a pallet, the cameras, our clothes, pieces of furniture, all of that has been on a pallet. Yet the pallet movement technology has been the same for the past 100 years. It's a pallet, generally the same size. It moves, it's made out of wood, and it's moved with forklifts operated by humans. It's the same thing.

And so there hasn't been an optimization on that for quite some time. And in our experience when I worked with Walty, we saw that the biggest problem hindering the manufacturing process was just trying to move material from one part of the factory to the other, and we were thinking, well, let's improve that. How can we build something that is super simple.

If you were to look at the need, the basic need of moving a pallet, we're looking at 3000 pounds, you're trying to move it from one location to the other. For you to do that today, you have to get a forklift. A forklift has to be at least 3000 pounds just to balance out because it's a forklift carrying something in front of it. It has to be at least another 3000 pounds, so you're moving 6000 to 9000 pounds, the forklift itself, and then 3000 pounds that you want to move. It just makes no sense. We're moving 9000 pounds to move another 3000 pounds, and we thought about it and we were like, OK, this is such an unoptimized system and nobody's touching it.

Ahmed Betel Mal: We're looking at designing a new primitive, something completely different. Let's rethink the problem. Our bots are 700 pounds. They can move 3000, but they're 700 pounds. So that's it. And it doesn't take a larger footprint than the pallet itself. Now when you start from there, I can move 3000 pounds forward, backward, left and right, up and down in full 3D with a 700 pound robot. That is a different math altogether, and that's the approach that we're taking with Mytra.

Now, if you limit the variability of movement to the 3 dimensions, now I can start to solve things with software. The bots can only do the 3 movements. And they can access any part of the warehouse within a structure. Now I can start to do things with software. If I want to build a conveyor, I don't have to build a physical conveyor. I can tell the bots move in the direction of a conveyor belt, and so it makes your warehouse completely software-definable, which is a completely new approach to doing any of this, and it applies to warehousing, logistics, manufacturing, anything that moves your standard pallet, and that's why we wanted to focus on the pallet because that's 90% of material movement around the world.

Chris Walty: Kind of my take on founder mode is effectively, as companies get larger, the gist is, do you still maintain your level of awareness and involvement in the details of your company, right? Very large company mode or manager mode, it's like, I fully rely on my team to do almost everything and I have this little function, maybe I'm just attending dinners and selling customers at a very high level.

I think in a company like Mytra, even though we're 74 people, Ahmed and I have no choice but to be in founder mode. There's just too much going on, there's too many aspects of the system. Every time a customer walks in, and we have one this afternoon, and between the two of us, we need to be fully versed in the technical stack. We need to be able to answer questions of like, why did you make this decision for this actuator or this design? What are the reliability issues? How do you plan to lower the cost, like things like that. If you can't talk intelligently about that, you're not going to drive confidence in these very sophisticated potential buyers or partners.

So for us, there's no choice but to be in founder mode, but as I think about what I love about this job, I love being in design interviews, so I love the product and the engineering aspects, and I know Ahmed does too, and that's something that as you get older, as the company gets more mature and larger, there's natural pressure to say you need to decouple from that, you need to let your team do that. And I think there's a balance that can be achieved where founders are still in the details.

Ahmed Betel Mal: Actually, the advice I would give to people trying to create their own company, really work in the field a little bit, right? Work in the field. You think you have an idea for some new service or product, work in that field and get insight. That's something I got out of my Stanford experience. You maybe you have an idea of what's really valuable and useful as a product, but the best approach to creating value is first that you get insight into what the problem is.

The reason Walty and I started working together is we met at the Model 3 production ramp, and we were customers of the systems that were there, manufacturing and logistics and material flow systems that were in production. We met there and we experienced insight. We saw what the problem really is. People go, hey, add more automation, add more technology here. Fine, OK, but the insight that we got that the fundamental problem that needed to be solved is moving the material.

Without you experiencing it firsthand, you miss it. You think, oh, I'm gonna work on warehouse robotics. You're gonna build this nice articulated arm, it's going to look nice and neat and great. Are you really solving it? No. And the reason you're not solving it is because you lack the insight into what the fundamental problem is. For us it was, oh, the thing that would unlock manufacturing is to move the material efficiently without stopping in a very reliable, safe way, you move it in that way, figure out a way to do it, and it would unlock value for manufacturing. Without that insight, I think you would be on the wrong path. So, my advice would be get immersed in the problem firsthand and then try to come up with a solution.

Ahmed Betel Mal: I'll give you another very practical example today. Everybody in the company today, everyone, almost no exception, has to work in the warehouse, using our system, interacting with the warehouse operators at our customer site. Everyone, even web developers, marketing, everybody has to do it. And the reason we did that is from my experience when I was at Tesla and building the software that I told you about, the software for the GA4 line, the way I built the software is I went to the line and I started working on the cars. I started putting bolts in and fasteners and the hood on the Model 3. And I experienced what the operator was experiencing, what kind of software was needed. What did I need to finish my job properly? And then I built that software, and that software worked everywhere at Tesla.

That is the insight that you need to get before you can say, oh, I'm gonna build a new manufacturing software. First, work as a user of that supposed system or product that you're trying to create. Get deep insight so you fundamentally understand what is the thing that you're trying to create, what is the value that you're trying to create, and then go, OK, I have enough insight that I know so much about this. I understand it at a basic fundamental level. Now you can come up with the new value creation after that. I think that's the best advice I could give before somebody says I wanna go and build hardware.

Chris Walty: I mean, if you look across the tech spectrum, most companies and Andreessen's famous quote, software will basically eat the world. It's very true. I think when you look at the economy, you realize that 85% of the economy or GDP is driven by physical industries, physical problems. 90% of the value in the tech economy has been created by mostly software. There's a huge discrepancy.

So when we look at the problems yet to be solved that haven't really been touched by software, they all involve some sort of actuation or manipulation of the physical world. IoT Internet of Things, has been around for a few decades, and that starts to solve some of the problems around hardware. It gives you signal back from the physical environment into some sort of brain or software, but ultimately, the actuation of that requires people or something else. But when you think of robotics as applied to the physical world, that closes the loop. Like I can build an actuator, a thing to move a physical item up, down, left, right. That's hardware, and the combination of that actuation closing the loop along with the software capabilities that can be applied to it. There's a ton of potential there, right?

This is the next frontier, the next several decades, you look around you, and you see all the inefficiencies in the world. You look at how the trash is picked up, you look at how houses and buildings are constructed, you look at how telephone cable is laid, you look at how physical goods must be manufactured, and there's a lot of opportunity for software applied to the physical world. And that's why all of us here at Mytra, we're super passionate about software applied to the physical world, and that comes in the form of robotics, and that is a hardware company.

Chris Walty: The second reason why hardware is perhaps more exciting today than it might have been a few decades ago, when you think of hardware, you think of silicon, like making silicon chips. That was really most of the hardware opportunity, but companies like Tesla and SpaceX have come along and they said, look, these are legacy industries. The script has been written for how to build automotive vehicles and what margins are for decades, and Tesla's coming in and saying, we're gonna do it differently.

Everyone told Elon and company they're crazy for doing this, this is very low probability for success. There's lots and lots of ways that this could go poorly for them, but they built an architecture that's significantly simpler, 10x simpler or more than the existing products, right? There's far fewer parts and shown that you can disrupt some of these industries. It's hard. Hardware is hard, but if done successfully and done correctly, there's huge opportunity to make meaningful advances in building electric vehicles.

For some people, it's much more rewarding than optimizing some ad on a website or increasing ad spend by 5% or something like that. So, there's been a bit of a blueprint and a playbook that says, you can disrupt very large industries through hardware, through some of the more modern successes like Tesla, SpaceX, Rivian, and a couple others like that.