He sold AI robots to Walmart & raised $150M. His #1 advice to founders? "Trust your gut." | Daniel Theobald, Founder of Vecna Robotics

Daniel's been building robots for 20 years. He's sold 100s of AI robots-as-a-service to Walmart, FedEx, & DHL amongst others. Last month, he raised a $40M round.

6 years ago, he realized  why robots weren't getting massively adopted. Builders like him were trying to build perfect robots that always worked. But every situation has edge cases. What if you designed robots to work only 80% of the time and use humans for the other 20%?

That one unique insight changed everything and was the reason he started Vecna. His robots can tell when they need help and ask humans to assist in real-time. 

Like most capital-intensive startups, Daniel played on hard-mode. Here's how he built hardware, robotics and AI, sold enterprise contracts, and grew to $10s of millions in revenue.

Takeaways

• Why as a founder you need to trust your gut and stick to your convictions.
• How to be objective and create an accurate model of the world to predict the future and make informed decisions.
• How to bootstrap even a capital intensive company in robotics

Keywords

Vecna Robotics, robotics, warehouse industry, automated forklifts, reliability, safety, customer adoption

(00:00:00) Intro
(00:02:05) The Beginning of Early Robotics
(00:06:00) Edge Cases in Robotics
(00:08:15) Robots Shouldn't be Perfect and AI Isn't Intelligent
(00:14:05) Technology Empowers Humans
(00:18:19) When Robots Need Help
(00:21:29) The First Pilot and Customers
(00:29:49) Covid Slowed Everything
(00:33:52) Finding Product Market Fit
(00:34:45)One Piece of Advice

Chapters

• 2:05 The Beginning of Early Robotics
• 6:00 Edge Cases in Robotics
• 8:15 Robots Shouldn't be Perfect and AI Isn't Intelligent
• 14:05 Technology Empowers Humans
• 18:19 When Robots Need Help
• 21:29 The First Pilot and Customers
• 29:49 Covid Slowed Everything
• 33:52 Finding Product Market Fit
• 34:45 One Piece of Advice

Transcript

Daniel Theobald (00:00)

Yeah, unambiguously trust your gut. Trust your gut as an entrepreneur, especially. I made some mistakes by allowing, I would say maybe less inspired people to convince me of things. In retrospect, I should have stuck to my guns and followed my gut almost every single time. It's important to listen, right? You've got to listen to people. Right. Being an entrepreneur is about creating the most accurate model of the world possible in your head. And you can't do that with misinformation, right? So you've got to be out there. You've got to be talking to people yourself. You've got to be understanding the market. You've got to be seeing the technology. You've got to be playing with the hands -on. You've got to really be involved with all the different pieces because it's all about creating that model in your head of the actual world and how it works. And when you've got that as an entrepreneur, that is the thing that is the most powerful tool you have, because now you can predict the future. If you have an accurate model, you can predict the future. takes a lot of courage in many ways to create that accurate model, because again, a lot of times it's unpleasant stuff. have to face realities, understand the customer, understand the product, understand the technology, understand how to motivate your engineers, understand how to sell to the customer. These are really important skills. Once you've developed them, then don't let somebody else who's less inspired than you convince you to do something different than you believe is right.

Pablo Srugo (1:27) 

Welcome to the product market fit show brought to you by Mistral, a sixth stage firm based in Canada. I'm Pablo. I'm a founder turned VC. My goal is to help early stage founders like you find product market fit. Well, Daniel, welcome to the show.

Daniel Theobald (1:45) 

Great to be here. 

Pablo Srugo (1:46) 

So you're the second robotics company that we have on this podcast. And I'm excited because robots are just inherently cooler than software. So it's always a fun time. So let me start at the beginning. mean, Vecna Robotics was founded in 2018. But as I understand, it's a spin -off of another company that you'd had since 98, which is called Vecna.

Daniel Theobald (2:05) 

Yeah. So when I was at MIT, I actually designed, as part of my master's thesis, this robotic operating system. And I built it in this cool new language that no one had ever heard of at the time called Java. Of course, Java eventually became a very, very popular language. But I was really excited about Java because it made writing good code really easy. And so I built this entire internet -based robotic operating system. I should have open sourced it.

Pablo Srugo (2:35)
  And this was when, like, this is like mid -90s? 

Daniel Theobald (2:36) 

Yeah, this was 95 to 98. Yeah, I really should have open sourced it at the time. In retrospect, would have probably moved the industry forward a lot faster. We're still kind of struggling with some of this things that I took care of in my thesis. Yeah. So I was really excited about this programming language Java. And so when I graduated, I was looking for companies that I could, you know, use Java and there was basically I found one and it was a health, it was a healthcare software company. So I worked for them for a while. were doing healthcare software for the military, but it was called W3 health. They don't exist anymore.

Pablo Srugo (3:12)

And they just happened to use Java.

Daniel Theobald (3:15) 

 They, yeah, probably for some of the same reasons that, kind of came out of academia and thought it was this cool language happened to use Java. So anyway, long story short, I worked for them for a while, but then I'm like, okay, I am winning the business. I'm doing the business and I'm sending all the money to these other people. I should just do this on my own. And so I started, this is when I started Beckner technologies and we did a lot of healthcare software for the military. But I ultimately used this to fund my robotics habit and applied for a lot of grants for robotics research. So back in the day, mobile robotics was not a thing that you could build a company to do. Unless you're a big robot arm manufacturer like KUKA or ABB that was building big robot arms for assembly plants that were building cars, this whole new world of robotics, just didn't even exist. So the place to get funding was the military. And so we  applied and won a lot of grants for doing robotics research in partnership with the military. 

Pablo Srugo (4:17)

What sort of robots did you build? 

Daniel Theobald (4:19) 

Well, so one of the early ones, like many companies, was a humanoid robot called the Bear robot. It's funny, you can actually go and look up videos of the Bear robot still. There even some funny memes about it, which is little bit inappropriate, so I won't mention. yeah, so the Bear robot was designed to...

rescue a soldier from the battlefield. It was a research project. Honestly, it was never going to be effective at rescuing soldiers from the battlefield because robots picking up human beings is just something that's, you know, we're a ways off from that. Yeah. Especially in a litigious society. You know, there are a lot of people who are really interested in like, you could do bed transfers and you know, you can help grandma and grandma, grandma and grandpa and then off the toilet. I'm like, no, 

Pablo Srugo (5:08) 

not so soon.

Daniel Theobald (5:09) 

That's a really hard problem. Interestingly, on that front, actually had a big group come and visit me from Japan. I think it was a combination of people from the Japanese government and from Toyota. And they said, you know, we've got this big problem in Japan. Our workforce is aging. We don't have anyone to take care of all of, you know, all of the older people who have retired and are going to need care. And, you know, after my experience with, you know, building this robot and experimenting with it. 

very clear on the fact that this was not the solution. And what they really need to do is focus on building robots that could build iPhones or whatever to get all the people who are building iPhones out of the factories and have humans take care of humans. 

Pablo Srugo (5:55) 

It's what? Like it's the dexterity, it's the edge cases, it's like all this stuff around, like that's what makes it hard? 

Daniel Theobald (6:00) 

Well, yeah, the edge cases. This is the thing you mentioned earlier about robots being cooler than just pure software, which is true but part of the reason for that is it's so much harder. Like, you know, you can write a piece of software and you can pretty much write a comprehensive set of tests for that software to verify that it does everything the way it should. Not so with robotics. As a matter of fact, not only can you not really do that, it's actually physically completely impossible to do that because the real world is messy. The real world is constantly changing in dynamics. So you don't know what combination of scenarios your robot is going to be faced.

Pablo Srugo (6:39) 

Just a question on Vecna, that company's around for like 20 years or so. Is it living off grants the whole time or did you at some point have some products that were also kind of driving revenue?

Daniel Theobald (6:51) 

No, sure. the way we built that company was through bootstrapping. So there are a lot of different funding models. We basically killed what we ate, ate what we killed, whichever way you want to say But the idea was that we weren't going to borrow money. weren't going to take VC. So we had consulting contracts with the US government, with the US military, with the Veterans Administration. We were doing a lot in the area of patient check-in kiosks. So back in the day before you had check-in kiosks at the airport, we were doing it for hospitals. And then that quickly transitioned into patient portals and that type of thing

Pablo Srugo (7:31)

So it was hardware and software and robots like it wasn't just robots with vecna .

Daniel Theobald (7:35) 

That's right So we were actually building physical kiosks at the time, but you know, I found that I wasn't super passionate about Health care software, you know, so I was really leaning in on the robotics side and at some point we got to the point where it really seemed to make sense to Accelerate the growth of the robotics business. And so that's when I decided for the first time. Hey, maybe we should bring in some outside funding to really, really go big with this robotic stuff. So that was around 2018, 2019. 

Pablo Srugo (8:07) 

What was happening at the time? What led you to see an opportunity for Vecna Robotics that made you comfortable with actually raising?

Daniel Theobald (8:15)

 Yeah, it's interesting because I had taken the approach that… I didn't want to bring in outside money until I felt confident that I could provide the kind of returns that I would want to see as an investor. know, so this whole idea of product market fit and how do we know when we actually have something was front of mind. There were real problems in the robotics, modern robotics, mobile robotics industry at that time, because it was still very much an academic thing. You have had a lot of companies out there building really cool robots, but they just weren't practical. They were too expensive. They didn't solve the right problems. They were fragile. They were great when they worked, but they did work most of the time. And so these problems were things that were really holding the industry back and were preventing product market fit for most of these companies. had a, an insight that kind of busted us through a lot of that. And that was that robots are never going to be perfect. And we kind of already talked about this. The real world is a hard, hard, messy place. And you talked about edge cases. It's all about the edge cases or what we might call exceptions. Robots are always going to run into situations where they need help, except for the most simple, you know, recorded playback robots, even that they need help all the time. Most companies were not accepting that they were trying to build robots that were perfect all the time. They're trying to build robots that always worked and, It's impossible. You can't do it. So once I was able to really internalize that fact and say, okay, well, what we need to do is build a system from the ground up that allows the robots to not be perfect. And what does that look like? So what we did was we first said, okay, if the robot's not gonna be perfect, number one thing is the robot has to be smart enough to know when it needs help. And that might sound trivial to a lay person, a human. Keep in mind that robots have no consciousness. They have no ability to reason beyond what an engineer programs into them. And so you'll see things like robots driving into a wall and just bumping and bumping into the wall because it has no idea. It's playing its program. And that's a little bit of a trivial example. Today's robots, wouldn't see many doing that. But they get stuck all the time because they're - They're trying to run their program. 

Pablo Srugo (10:39) 

Well, funny enough, the vacuum that cleans my floor today does exactly that. So it's not totally solved yet.

Daniel Theobald (10:45) 

Yeah, exactly. So how does a robot know when it needs help? That was problem number one for us to solve. And we really dug into that and figured out a number of ways to determine when the robot was not making sufficient progress on its assigned mission. Again, easy to say, very hard to do. Once we got that, then the next step was how do we give the robot an ability to ask for help? Also sounds really easy, but it's difficult in the real world where you might not have good connectivity, know, things like that. So it's a combination of these different levels of support that you'd need. So the first thing the robot will do if it  realizes that it needs help is it will try and send a message to a support center. So it's basically ET phone home type of thing. Robot calls home and says, hey, I need some help. I'm not making progress. I'm not sure why. So a human can then add our what we call pivotal control center. Pivotal is sort of our command and control software. A human being at our pivotal command center can log on to that robot and very quickly assess the situation. What's going on? Why is the robot not making progress on its mission? Most of the time, those interactions only take about 30 seconds because a human is an amazing machine, super, super smart and able to figure things out in ways that computers and robots are still a long ways from. Even people see things like chat GPT and AI and they think, robots are going to be smarter than humans.

No, that's bullshit. Sorry. Can I say that? 

Pablo Srugo (12:28) 
 Haha, Yeah, that's fine. 

Daniel Theobald (12:30)

What they don't realize is that these, these modern AI systems and you know, people are working on this, but these modern AI systems, there's no intelligence at all. I mean, which is why we call them artificial intelligence. But I think artificial intelligence is even a misnomer. It's confusing to people because they think that implies that there's actually some intelligence there and there's literally zero intelligence. Chat GPT for instance, is just a huge mathematical engine. It's just a big statistical engine that's sucked in all the text off the internet. And you give it a bunch of words, basically, I'm oversimplifying, but you give it a bunch of words and then it will predict what the next most common word is in the English language. 

Pablo Srugo (13:08) 

You think all the talk about AGI is like overhyped? 

Daniel Theobald (13:10) 

Well, it depends on the details, right? But yes, if people are worried about artificial intelligence coming alive and… fighting against humans to protect us from ourselves and then ultimately realizing that we're lost species and killing us all, not gonna happen. It could happen that way, but there will be a human behind it. It's not gonna be an artificial intelligence. Yeah, we've watched way too many movies, honestly. 

Pablo Srugo (13:37) 

What about it just like being able to replace like so many jobs that today, like that's the thing about a job, like you have your 80 % and then you have your edge cases and… That's the beauty of human is it can handle the 100%. And that's the job. And even today, chat, GTP’s got a long way to go. You know, it can write, but it actually can't write as well as you'd hope and all these sort of things. But you could see how soon it could do 70%, 80%. But then the edge case is it would break unless there was like AGI. 

Daniel Theobald (14:05) 

right. And this has always been the case for technology. It's accelerating, no doubt. But the idea is that technology empowers humans to do more.

It empowers us to get more done in the same amount of time. And we tend to forget this.I mean, there's so many examples throughout history, all the way back to the looms and the luddites who were afraid that, you know, this new automated shuttle that could allow us to make textiles faster was going to put them out of jobs. No, it allowed them to make more textiles less expensively. People didn't understand that they jam their wooden shoes in the looms to try and sabotage them. Hence the word sabotage from sabote for the wooden shoe, but it never seems to actually turn out this way. Now there are a lot of people, you know, and there are even studies at MIT saying, well, but this time is different. And this time we're going to have massive unemployment. I think that almost lacks a little bit of imagination. You know, there was the switchboard operators back in the day, right? It used to be that if you wanted to place a call, you picked up your phone, you turned a little generator that generated enough electricity to ring a bell. at the phone operator station, wherever that was, you know, a few miles away maybe. And then a human being would plug into your, you know, cause a little light and low bell would go in there, it'd be a little light next to your plug. They'd plug in the headphone. They'd talk to you and they say, hello, Mr. Jones, you know, who can I connect you to? And they'd say, I want to talk to Tom. So then they look up, they'd figure out which Jack was Tom's and they'd plug you into Tom's, right? That's how it worked. Somebody came along and said, hmm, maybe we could build an automated switching circuit to connect these calls automatically. And once again, there was like, but you're gonna, you know, you're gonna destroy jobs with this automation.  And it didn't even really have a negative impact in jobs in the sense that people retired out of those jobs, just new people didn't go into them. The thing that I think is even more interesting though,is if we were to have protected those jobs and say, no, this automatic phone switching circuit is bad for humanity. We're not going to allow it. It would take many, many, many times the population of the entire earth to handle today's communications using that technology. And it would suck. 

Pablo Srugo (16:36) 

Which is another way of saying we never would have had today's communications because it wouldn't have been able to get together. I mean, you would have just basically frozen the world in a certain in a certain space in the world. 

Daniel Theobald (16:46) 

Yeah, it would have been all steampunk, yaknow, steampunk world, which would be cool. I do. I do have some nostalgia about that in some sense. But, you know, and then just one more example. We've had the internet for what a couple decades more or less. I heard a statistic recently that something like one third of all jobs are somehow social media related. Social media didn't even exist two decades ago. And now like,

Massive massive employment has been created because of this. So again, these are just all examples to say It's easy for us to see something we're losing but it's really hard for us to understand something we're gaining and we as a species have universally almost always gained more than we've lost through technology Technology is a great equalizer as well. And that's one of the things I get most excited about we have more equity We have more access to funding. We have more ability for people to be treated fairly around the world, fewer human rights abuses because of technology. It's not to say that technology doesn't have its dark side. It always does because they're humans that can use it for good or evil. But by and large, AI robotics, these things are not to be feared, but we need to be thoughtful about how we use them.

Pablo Srugo (18:02) 

 So going back, you one of the questions I had is as you mentioned your insight in kind of these robots, knowing that they needed help. And you said in a way where it sounded really simple, like it knows what it's not doing what it was supposed to do. So it asks for help, but I'm sure it's quite complicated. And it's very important to like what you end up building. like, what's an example of that? And what are like some of the harder examples of a robot that would be doing something, realize it's not getting it done and then realize, okay, I need to ask for help. 

Daniel Theobald (18:32) 

Yeah. So one example would be we have an automated forklift. It was given a mission to go pick up a pallet Let's just say pallet location, one, two, three, navigate successfully all the way across the warehouse. All right. So it's making progress on its mission. It gets to a pallet location, one, two, three, and it starts searching for the pallet. Right. And this is where this is one of the areas we use AI. I'm air quoting, but AI where we use machine vision and it try, you know, and it uses deep learning to try and find a palette. Cause pallets tend to look. similar to each other, and then it's not able to find the palette. So the robot doesn't necessarily know why it's not finding the palette, right? Because it's not intelligent. It may be that the deep learning AI algorithms are just not matching on this particular palette for some reason. It may be that the palette's not there. It may be that the palette's there, but it's shifted to the side. got knocked, you know, far enough that it's outside of the detection. So at some point the robot tries, you know, a tries B at tricycle. have like a lot of built in recovery mechanisms. It may move a little bit, try and look at it from other angles. At some point it says, I'm not, I'm not making progress. I've wasted too much time trying to find this palette. I don't know what's going on. And that's what it makes the call. So as human helped me and then the human logs onto the robot and can look at all the cameras. can look at the history, right? It's got the history of the camera so it can see what's happened so

and can then determine what's going on. And it may be, they said there was a pallet there, but there's not a pallet there. So then that's when we would have to cancel that mission, notify the warehouse management system that there wasn't a pallet where it told us that there should have been one and, you know, go to the next mission. It could have been that the pallet was damaged. So this is a good example. Pallet was damaged to the point where the robot's not able to detect it or pick it up. In that case, we would call in what we would call a local assist. Local assist, right? So all this help is happening remotely. It could be, you know, anywhere in the world that a human is helping the robot. The local assist is okay. The remote human says, Hey, this is a problem that we need somebody at the warehouse to solve for us. So they may come and, know, there might be a big piece of wood that's, hanging down in front of the pallet fork pocket. That's jammed things up. They may have to come and like physically fix that because the robot doesn't have, you know, hands to do it. And once they do that, then we, you know, then re -engage the robot to finish its mission. In the meantime, we might've sent it off to do something else because you always want to keep your robots busy and then, you know, come back and redo this one when the time is right. So I'd say that's a pretty typical type of example of how when you give the robot the ability to know it needs help, you give it the ability to ask for help, you create a situation where a human can help it, then we keep the robots moving and they don't have to be perfect because they never will be. 

Pablo Srugo (21:29) 

Got it. So that makes sense. So you have that insight. Where do you go from there? And like, how do you go from there to raising? Cause I think that route was about 13, 13 and a half million if CrunchBase is right. Like what happens between those two things? 

Daniel Theobald (21:43) 

Yeah. So we went out and caught a bunch of customers. The idea was that once we realized that our robots didn't have to be perfect to provide value to the customers, we could start serving customers immediately. And this was really important to get out there and start that flywheel of feedback, right? Cause you're never going to know if you have product market fit until you actually have product market fit, right? So a lot of times people will do a lot of, you know, market analysis and surveys and they'll do a lot of assumptions and projections. And that's, that's all great. That's not the way I tend to roll. tend to be more, you know, hands on experimental actually try it, but you're never actually going to know if you've got product market fit until you've got customers that are saying, yeah, we want to buy more. We want to buy more because you are providing value to us

The return on investment, the ROI is a really important part of this. But one of the things we're able to convince a lot of our early customers of was that we'll get close on the ROI, but a big part of your ROI that you need to consider is that you need to learn how to adopt robots into your operation. And that is something that is going to take time. It's going to be something that your organization will need to adapt to and learn. And that's part of the ROI you're going to get out of the servo process. because. I think it was becoming pretty clear, particularly with COVID and labor stuff and the fragility of human workers. a lot of cases, the warehouses, a lot of them were just shut down because they couldn't get workers. So there was this real understanding that we need to learn how to adopt this type of automation. So that was a big part of the value proposition. 

Daniel Theobald (23:21) 

But that was later, right? Because in 2018, you would have, or where did you even have the, because you had the insight, when did you have the product idea itself?

Daniel Theobald (23:29)

It was all along there. We had been building the driverless forklift. We had a grant from the Navy, I believe, to do autonomous forklifts, but it was more as a research project. 

Pablo Srugo (23:41)

OK, so you had that product. then you saw the warehouse opportunity, which I'm sure was pretty obvious at that point.

Daniel Theobald (23:48) 

 Yeah, and moving pallets is an absolutely massive market. If you look around even the room that you're in. Pretty much everything you see spent part of its supply chain life on a pallet. Like pallets are the way our physical economy moves. And so, you know, it just was obvious to us that there was a big, big opportunity there if we could build a solution that allowed customers to move their pallets reliably, even though the real world is messy and robots are perfect. And we went out to a lot of the big players, the FedExes, the Walmarts, the DHLs, You know, you name it and got a lot of interest and started doing pilots out there. 

Pablo Srugo (24:27) 

It's such a big undertaking to all of a sudden put these massive robots in, you know, your warehouse or whatever and start moving pallets with them. It's not like this is again, where software and hardware or, you know, bigger robots, there's such a big difference. Like, Hey, you want to try out my software? Like, sure. Here's an account, like play around with it. Right. And, and they get to try it. This is different. So I'm just curious, like you had these conversations, you get some interest who actually. Bites a bullet and decides, you know what, let's do this. And what does that first pilot look like? 

Daniel Theobald (24:54) 

Yeah, it was a combination of sort of the big customers. FedEx was one of our first customers. And NC Small Local customers as well. We thought it was valuable to have that mix, you know, because we wanted to be able to ensure that the product would operate in a wide variety of situations. Actually, one of our first pilots was with FedEx. It was with automated tuggers. A tugger is basically a forklift, but without the forks, instead of carrying pallets on its forks, it will carry pallets or other things on trailers behind it. You could almost think of it like a train without tracks. But your point is a great one because big robots are very different than, download this app on your phone and see if you like it. Big robots are heavy and they move and they could run into things and they could damage things and they could potentially hurt people. So safety was one of the big hurdles that we had to overcome, which was both good and bad from a business point of view. It was good from the perspective of once we solve these problems, it created a very large moat around our business. because, know, getting safety certified proving, you know, to 99 .99 % reliability that the robot would fail in a safe way. call this performance level D. took a lot of time. took a lot of money and took a lot of engineering. And so this is one of the things that bringing in the outside funding helped with.

Pablo Srugo (26:22) 

And walk me through like, what are like, we talked about ROI earlier. I'm curious, like, what is the the I like, what's the investment? How much does it cost to, you know, round numbers, whatever, like, to outfit, to buy one of these robots or to outfit the entire warehouse with these robots? What are we talking about? 

Daniel Theobald (26:37) 

You know, at the time, robots were anywhere from 100k to 150k, I think is you know, a reasonable number. yeah, you're not going to go and just buy one for yourself. you know, a hundred, $150 ,000. I mean, if you think about what you're paying people to drive where forklifts, on a single shift, and then you multiply that by three shifts, you know, you get there pretty quickly. So that's like the individual robots and, and then, you know, you have the centralized control system, you have all of the deployment activities, you know, maybe double it for the first year. So for every robot add an additional $150 ,000 to get everything set up. And of course it scaled nicely when you had more robots. Eventually we moved to a robot as a service, our AAS model, where customers had the option of just paying sort of a monthly fee for the ability to use the robot versus actually owning the robot themselves. And I think most of our sales these days are now robot as a service, which is really cool because you can essentially get immediate payback. You can start saving money in the first few months easily. Whereas the industry, took a while for them to start to have interest in this because they were just used to buying equipment. This is one of the really interesting conversations I'd have with customers though, because most of these buyers were like warehouse managers and that type of thing. And they're used to buying a forklift or buying a conveyor belt. And the way those things work is you buy a piece of equipment and you You know, you pay it upfront and you amortize it or you depreciate it over, know, five years, 10 years, and you calculate that, you know, year two, three, you're going to have a return on that investment. But, those pieces of equipment, they always start out great and then they degrade over time, right? Their performance gradually gets worse and worse and worse. Yeah. The interesting thing was that, our product would get better and better and better. This was that convergence of software and hardware that nobody had ever experienced before. Like we were one of the first companies to be able to give you a new release of your hardware operating system. And so we deployed robots at Walmart, for example. And I think it was within the first two months, the robots were already just through software updates. were already operating 18, they were getting 18 % more work done than when we had started. And this is cause the robots- - And really the system as a whole and the engineers, every we're learning as the robots are out there, we're able to constantly improve them, make them better. So that was really exciting thing, but it was hard for people to kind of wrap their heads around that idea. They're like, no, I'm just buying a product. I'm like, no, you're buying a solution that will get better and better and better. And we'll learn over time. And that makes it more valuable to you. can't calculate your ROI today based on what the robot can do. I mean, you can obviously. But you shouldn't just calculate it based on what the robot can do today because the robot will be able to do so much more in the future as well. And that should be taken into account at some level. yeah, anyway, I mean, we got to the point where the customers were saying, hey, you know, can we buy more of these? 

Pablo Srugo (29:48) 

That was my question. Like how quickly do things take off? You do that pilot with FedEx, talking about a hundred, 150 K, you know, per robot, you figure out all the safety pieces and then do sales just kind happen like 2019, 2020, or is it a bit trickier than that?

Daniel Theobald (30:04) 

It's a little bit tricky in the sense that COVID happened. So COVID ended up putting a big pause in some sense in most companies adoption of this type of technology at the beginning. People argue that it may be accelerated at the end. think in retrospect, maybe that's not entirely true. Most of the large organizations at the beginning of COVID in 2019 basically

retrench, they basically said, we need to go into a capital preservation state. And so they basically shut down all of their innovation projects at the time. So that slowed things down. But I mean, prior to that, we were at this point where, you know, people were saying, yes, these are great. There are challenges, right? There are still, you know, some safety challenges. are still some challenges with, you know, picking up mostly what were crappy pallets. We could pick up good pallets all day long. No problem. But there are a of crappy palettes out there. So there were some challenges with that. But we're at the point where customers were willing to place larger orders. 

Pablo Srugo (31:07) 

And I guess customers were buying multiple. So you had million dollar orders and things like that, would think. yeah 8-10

Daniel Theobald (31:11) 

Yeah, 8-10. 8-10. I mean, we had a 50 robot order with Walmart pretty early on. But yeah, they were one of the ones that they said, love you. And it really hurts us to do this. We don't want to do it. But we've basically been told by corporate, we've got to shut all this stuff down for the time being until we get a handle on what's going to happen with COVID. And so it took some time to come out of that. yeah, mean, basically, the adoption of the technology continues to be a struggle, but it's mostly just, again, this issue of customers learning to adapt their operations to robots. The robots are really great, and they can do all kinds of tasks incredibly reliably. Right now in most cases, it's like, okay, how do we now incorporate them? So for example, one thing that tends to be a little bit of a challenge is you're a super good forklift driver and you can kind of squeeze your forklift between two things with like maybe an inch to spare on each side. We're not allowed to do that with the safety standards, right? So it's not that we couldn't do it, but it's just like these safety standards say, You shall not do that. We have to have a certain, you know, certain clearance around all sides of the robot. So this is just a practical thing where like, you know, some warehouses were not designed with quite enough space. So, you know, they figure out, we can use it over in this area. Maybe we can open up the racking a little bit and whatever. but these are some of the things that you wouldn't necessarily think of. You know, when I tell people we've got automated forklifts and they work great and they're reliable and they're safe, they're like, well it can do anything a human can. Well, it almost could do anything a human can if we allowed it to, but we expect robots to be way, way safer than humans. And so we actually put all these rules around what the robot's able to do that we don't really put around human operators. so, you know, it's, it's, it's an interesting, sort of an interesting thing to, be held to a much higher standard, than, than the human operators.

Pablo Srugo (33:18) 

 How many of these like warehouse robots are out in the world now?

Daniel Theobald (33:21)

Hundreds, yeah, we've got them deployed at, I'm not sure which ones are public and so I won't mention too many, but they're out at a lot of different name brands and common household brands that you've heard of, places you shop and products you buy are using are apparel, beverage, hardware, automotive. You know, we're sort of in all these different industries and people are moving thousands and thousands of pallets every day using the technology.

Pablo Srugo (33:58) Perfect. Well, we'll stop it there. I'll ask the two questions we always end on. The first one is when did you feel like you had true product market fit? 

Daniel Theobald (34:08) 

You know, I think the first time I felt that was when I was out at a deployment at Walmart, as a matter of fact, and- and just saw the robots working. And then we made a video that just showed,I mean, this is like a million square foot or maybe it was even two million square foot warehouse. And watching that video of our robot, like just do its thing, it was thrilling. It was absolutely thrilling. Because you can imagine the years and years and years and actually in some sense, the decade of work that had gone into getting to that point, just absolutely thrilling. And there's always more work to do, right? These edge cases were always making this determination of where do we invest our engineering effort next to make the robots even better. But yeah, that was an exciting time.

Pablo Srugo (34:56) 

the final question is, if you could go back in time and give your younger self one piece of advice, what might that be? 

Daniel Theobald (35:03) 

Yeah, unambiguously trust your gut. Trust your gut as an entrepreneur, especially. I made some mistakes by allowing, I would say maybe less inspired people- to convince me of things in retrospect, I should have stuck to my guns and followed my gut almost every single time. It's important to listen, right? You've got to listen to people, right? Being an entrepreneur is about creating the most accurate model of the world possible in your head. And you can't do that with misinformation, right? So you've got to be out there. You've got to be talking to people yourself. You've got to be understanding the market. You've got to be seeing the technology. You've got to be playing with the hands -on. You've got You've got to really be involved with all the different pieces because it's all about creating that model in your head of the actual world and how it works. And when you've got that as an entrepreneur, that is the thing that is the most powerful tool you have, because now you can predict the future. If you have an accurate model, you can predict the future. you can say, if I do X, what will happen in the future? And if you've got a bad model of the world in your head, which a lot of people do a lot of CEOs do, which is why they make bad decisions, then. you're not going to be successful. It takes a lot of courage in many ways to create that accurate model because again, a lot of times it's unpleasant stuff. You have to face realities. You know, just like this reality I had to face that the robot was never going to work reliably all the time. So like, okay, I could fight that. I could be upset about that. And it was really interesting because I had a lot of people totally neg on me about that. It was like, you know, well, if your robots really worked, you wouldn't need this. And, you know, I just got that all the time, mostly from the academic community. They're like, you know, you're cheating. I'm like, yes. And in business, if you're not we're value. That's right. Delivering value. That's right. If you're not cheating in business and cheating from the engineering perspective, you know, I don't condone dishonesty, fraud, but if you're not cheating, then you're, you're, you're navel gazing. You're, you're, you know, you're pursuing academic interests, but you're not actually providing value. So that's really what it was all about.

Trust your gut, but you're going to trust your gut because you've done the work to figure out how the world works. Understand the customer, understand the product, understand the technology, understand how to motivate your engineers, understand how to sell to the customer. These are really important skills. And once you've developed them, then don't let somebody else who's less inspired than you convince you to do something different than you believe is right

Pablo Srugo (37:39)

We'll stop it there. Thanks Daniel for taking the time to share your story with us. 

Daniel Theobald (37:45) 

My pleasure. Super fun. you know, certainly anybody who's interested can reach out to me. Always happy to help any way I can.

Pablo Srugo (37:54)  

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