- Gecko Robotics builds wall-climbing robots and enterprise software to maintain and protect essential infrastructure. - He is the CEO and co-founder at Gecko Robotics. Jake, thanks for joining me today. - Every hour you save, you're saving potentially millions of dollars. - Hardware is hard. - Be very careful. - There's a lot of really important problems to solve.

- There's so much sex appeal to building new things, but you gotta get the business model right. - The business model has to make a CEO or CFO give up. - There's a huge fire going on right now at Philadelphia Energy Solutions. Oh my gosh, again, look at this guys.

Look at this video right now. - Today, the Navy remains a formidable fighting force, but even officers within the service have questioned its readiness. - At a missile silo we visited, time and frigid weather had clearly taken their toll. - Developing right now, gushing for hours with no end in sight, thousands of barrels of crude oil spilling from a tank.

- The report does an estimate of what the need is to bring the overall grade up to a B, which is what the society sort of determines to be adequate. And it's like 4.59 trillion dollars. - We'll see you next time. - We'll see you next time. - We'll see you next time.

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- We'll see you next time. - Yeah, all right. - Hi, I'm Jake, the founder and CEO of Gecko Robotics, a company that makes robots and software to help diagnose the health of the built world. Now, it started in a college dorm, my college dorm, is now a company that manages over 500,000 of the world's most important and critical pieces of infrastructure.

Now, the structures that we use to power civilization have reached their useful life. It's a huge problem and it's getting way worse. But it's a problem that you probably don't think about very much, but you should. In New York, for example, there are over 17,000 bridges, most of which are in New York City.

And guess how many of those bridges are not in need of immediate repairs? Only six. See, maintaining things has always been an afterthought. But that afterthought is now a $4.59 trillion domestic problem. And by the way, it's getting worse. It's holding us back. For example, the military spends 40% of their budget, over $400 billion on maintenance.

Not on building new things, just keeping old things working. And Fortune 500 companies will lose $1.5 trillion every single year because of catastrophic failures that were unpredictable. And our best defense to stop that from happening hasn't changed in over 60 years. It's this. This is Joe. And Joe's on a rope.

Now, Joe's armed with a handheld sensor and what looks like an excruciating wedgie. Now, Joe's our best chance to ensure that pipelines don't explode, that bridges don't collapse, that dams don't fail, and that airplanes don't disassemble mid-flight. It's an impossible job, unfortunately, for Joe. You see, we obsess about how software has changed everything for everyone.

It's eating the world, right? It's important to remember that for the guys behind me, it's actually never helped them. You see, the data that we need to prevent catastrophes from happening in the built world simply doesn't exist. And without data, what can software do? Now, I became obsessed with this problem in college.

I was studying electrical engineering, and my obsession for energy took me to a local power plant in Pennsylvania. I wanted to see how power was made. And so, I decided to dive in head first. No, I actually dove in head first straight through this hole. And when you got through this hole, you got into a 200-foot-tall steel-tubed box, the length and the width of a football field.

This is a power plant boiler. And the boiler's job was to turn water into steam by getting really hot. See, the problem is, as the plant manager, Jeff, told me, that 40% of the time, this boiler would be shut down because of pressure tube explosions. It would cost them $2 million every single day they were down.

This is a small little power plant. And so, I asked Jeff, "How do you stop this from happening?" And he says, "Well, we send up humans on ropes looking for invisible defects." And then he began to tear up. And he told me a story about how his best friend fell and died the year before doing one of these inspections.

And he fell and died in the exact spot I was standing. So, I was floored by the story. And so, I had to do something about it. So, I went back to my college dorm and started building the first wall cleaning robot. And I armed this robot with ultrasonic sensors, just like doctors use for sonograms.

And I deployed that robot into the boiler, saving the plant manager, Jeff, 30 million dollars just that year. And I became absolutely obsessed with how we understand the health of the built structures that we use every single day. That's why I started a company. And I boot shopped that company for three years, pouring my life savings into it.

I slept on my best friend's apartment floor. And I was down to the last hundred dollars. Two things happened within two weeks. First, I got an offer to buy the company from a company that makes power plants. And then second, two partners from a group called Y Combinator said that if I stayed poor and kept on building the vision, that one day Gecko would change everything that we knew about built structures.

So, I decided to stay poor and keep building the vision. And so, we launched the company in 2016. And we began to deploy the technology into the oil and gas, manufacturing, public infrastructure, and even defense sectors. We had to build robots that could climb and traverse and get sensors into all different kinds of surfaces, geometries, conditions.

And once you have 500,000 assets that you have to climb around, you begin to iterate your robots really, really well to be able to handle these kinds of environments. And it became clear that contrary to popular belief from the VCs at the time, the robots were the mode because they could get sensors to places that could never be gotten to before.

We could convert atoms into bits. And so, I wanted to double down on that mode. And so, we started building robots that could fly, swim, crawl, and walk up any surface. We began to build autonomous platforms to arm those robots to be able to go to places so that humans didn't have to be in dangerous environments.

We built and became the best in the world at building ultrasonic sensors, electromagnetic sensors, as well as lasers to be able to see and understand what was going on inside of steels, composites, and concrete. We built fixed sensors that could stream live information and data sets to us, both the health, but also the operational conditions of those assets itself.

And we built an API platform for robots called Fulcrum so that other robotic companies could actually be used on our platform in streaming data and information live to our customers. And after 10 years of collecting data on almost every structure imaginable, we launched cantilever. Our AI and robotics powered operating platform to put those data layers to use for our customers.

You see, when you start building software by first starting out with the data layers and then building up, you're severely advantaged because you can build software from first principles. And our ontology now is able to affect folks from the ground level, the guys on the ropes, all the way through to the executive suite.

It was extremely powerful. But I was just talking about it. Let's actually dive into an example. So to do that, I'm going to take you to Georgia, to a manufacturing facility that me and you use in the bathroom every single day. Now this facility has thousands of assets and billions of dollars worth of infrastructure.

And so they wanted us to prove out over 50 assets, what we could actually do. So I'm going to take you through one of those assets today, a sulfuric acid tank. So first, what we do is we gather information about the asset by customers sending us their metadata. And we build out a digital representation of that asset inside of cantilever.

And then we send in our robots. First, we use a drone. You can see over here. Now the drone is armed with cameras that are doing a photogrammetric scan of the asset. It enriches the asset model itself. Being able to identify different kinds of defects using point clouds. So corrosion areas like over here, we're able to categorize and locate.

And then dents and cracks as well, enriching the data asset model. We want to go further than that. We incorporate other sorts of components like piping and pumps that you see here, both in the inlet and outlet. This is extremely important and valuable because we keep on adding data layers.

And the next one is a dog. Come here, boy. Good dog. Hey. Everyone say hi, please. Yeah, there we go. Nice. So let me pet him real quick. He likes to be pet. Yeah, good boy. So this dog will walk around to dangerous environments, gathering information about what's going on on the infrastructure.

Now, what's important also to understand is that because of an API platform for robots, we've built an extensible way for a company like Anybotics, one of our partners, to be able to gather information and data sets. And this robot is extremely exciting because it's built to be explosion proof, meaning it can go inside of oil and gas facilities and nuclear facilities and beyond.

It's gathering information like you see above, thermal imaging, to understand what's going on with the asset. All this data is really important when we do optimizations later. And we want to begin to continue to gather more information about the asset. So we send in submersible robots. These submersible robots are looking at the deformation because of the weight of the liquid, as well as the health of the asset's floor, to prevent things like that oil tank or leaking into rivers.

And once we've gotten this, and the customer is really excited because we can do this while they're online, we then send in our robots and we rise, collecting information and data while the customer's tanks are still in operation. Now these robots that you see right here are armed with ultrasonic sensors, cameras, and the IMU on board to ensure that we can do this autonomously, gathering terabytes of data in 12 hours for this tank, a process that used to take about a month to do while the asset was shut down, costing millions of dollars.

And we can do this in a way as well that ensures that we can localize data points to begin to run optimizations. And in this case, because these assets are supposed to be reaching their useful life or reached it, we can extend the useful life of the infrastructure. And so for this example, we can tell them what to fix in five and ten years to extend the useful life so that this asset continues to be able to do its function, opposed to having to replace it for eight million dollars, which was what the plant thought they would have to do.

So once we do that predictive model, we work with maintenance companies to ensure that they actually take action on that, and we update the model to ensure that a source of truth remains. Next, the customer actually wants us to begin to do other optimizations, so we use fixed sensors like this that'll stream information about not just the health, but also the operating condition of the facility.

You see, when you're running a, let's say, a big manufacturing facility, your goal is to figure out how to make more product without having stuff blow up because of a new operating condition. Now that's never before been possible because the data you've been able to work with has been from Joe on a rope.

And so you don't know if you change your throughput or make more product, if that'll destroy the assets, we're able to run optimizations. Now I'm going to show you that here. So this customer was able to, because we're streaming information and data from the pumps and from the asset itself, we were able to figure out how to increase the throughput or make more product by about five percent more while only having to incur over 90 days an accelerated damage of the asset of about two months, equivalent to two months.

And so we've proved that we could do that by actually lowering the fill heights in the tank and increasing the asset concentration level. You can see the optimization being run right behind me. Now this was significant because of the ability to not just extend the useful life, but actually produce more while not having the potential risk of a catastrophic failure, something never before possible for these companies.

Now let's talk about the outcomes for the customer. On average, over the 50 assets, we extended the useful life by 10 years. This affects their P and L and their margin right away because of ability to extend your depreciation models. And then we created $105 million of value by being able to reduce safety risks as well as environmental, as well as being able to reduce the amount of capex the customers needed to spend.

And the estimated from the customer was a four percent impact to their margin. Now all of this optimization and information coming in doesn't just help the customer, it also helps cantilever be exceptional in a compounding way at running facilities more efficiently. And so now you have an ability to have an unfair advantage from companies that are not utilizing technology like this.

So not just are robots cool, but they're actually solving a business problem. So this has been flying off the shelf, as you can imagine, since we launched cantilever this year. The 12th largest oil and gas company in the world, for example, determined that they have 100,000 tanks and that we could provide $122,000 of ROI per tank.

Now initially we signed a $30 million contract, it's exciting, it's going to extend to $100 million, but it shows how if you adopt technology in a way like this, it's unfair. Now on the defense side, we're working with the Air Force on $130 billion modernization program. Now they have to modernize over 400 nuclear missile silos, and the best way to determine how to modernize or what the scope was to improve the missile silos was, I kid you not, Joe on a rope with a hammer, who was listening to the sound that the silos made when he hit it.

So now Gecko is helping to improve what the modernization scope actually should be, and it points out something interesting. Those that are determining the scope and size of these modernization projects are the same ones incentivized for that amount of dollars to be as high as it can be. Now on the Navy side, one of the biggest problems is only a third of our ships are available to patrol and deter conflict around the world, and the reason why is because of maintenance cycles.

So we worked with the Navy to improve, in this case it was Joe on a skateboard on his belly, over a flight deck looking at different areas trying to gather information and data sets. We improved that to be able to reduce labor by 85 percent and improve the turnaround times for flight decks alone by about a month.

So now we're doing tens of millions with the Navy on flight decks and we're extending that to ballast tanks, hulls, as well as commercial maritime. It's really exciting. And then on the energy and manufacturing sector that's where most of our seven to eight-year accounts lie with big contracts like Exxon, BP and beyond.

Now one thing that's extremely exciting is that it turns out if pipelines explode or when oil leaks into rivers it's pretty bad for the environment. So the studies show that by 2030 in the U.S. you can reduce emissions by about 18 percent if you can stop those kind of things from happening.

So technology is available today to make a drastic impact on net zero. And then it turns out as well if you're the best in the world of understanding the health of built structures you're actually very advantaged in building new things and so that's what the admiral in charge of 132 billion dollar nuclear submarine project called the Columbia class determined.

So now we're helping to create the most advanced submarine in the world from the beginning to the production end. And it gives you a peek into what's coming. You see, I'm not crazy. Building robotics, material science, AI, software, sensor company, it's really freaking hard. But I had no choice.

You see, the promise of AI from AI companies to make impacts in these industries have gone empty for years and years and years. And it's no wonder why. They're building their foundational models off of Joe's data. Data that looks like this. This is a real report from one of our customers before they used Gecko.

It's no wonder that AI hasn't made the impact of the promise that it was supposed to. So this is why we built Gecko and why I believe because of software being commoditized that first-order data companies will dominate the next 10 and 20 years in software. And my journey through the rust has given me both a pragmatism and optimism about the future.

A future where understanding how things work helps you build new things. Understanding how to use AI and robotics in these real practical ways. A reality where we can understand the health of the built structures all around us just as well as we understand our own health. And you begin to see robots, of course, in normal society.

But these robots won't be built for doing backflips or folding laundry. They're going to be built to help realize the impact of AI for the built world with systems like cantilever. Thank you. David Sachs showed up, everybody. Doggy. Hey. Sachs, that's a robotic dog. How's your coffee? Your kind of dog.

Oh. Go to Sachs. Go to Sachs. Go give him a kiss. Oh, there we go. Sachs is very affectionate. You can pet him if you want, David. You can pet him, Sachs. Here. Where do you pet him exactly? There you go. That was a lot of love. Yeah. I am experiencing companionship from this dog.

He's excited. Yeah. It's definitely, it's definitely a nice dog. Yeah. Nice dog. Doesn't bite. Jake, I think one thing that would be great, based on the kinds of customers you have, can you tell us a little bit about the sales life cycle and the type of deals you do?

I mean, it's so interesting to, is it like an enterprise software type sale? And you know, when you're going in and doing a physical workplace. I mean, I didn't know where to sit. Were you worried about the dog? No, this was, it's a very poorly organized conference. Yeah. Let's talk about that.

We only told you where to sit five times in the last four minutes, but. I don't understand these images. I have, you know. Literally yesterday, we go in there after and like, Sergei comes, he does his first thing. And I'm like, Sergei's like, oh yeah, do you have any food?

I go out to the food and it's just like rubber conference checking in the VIP speaker area. And I'm like, Freyberg, can we just get some sushi from Nobu? We should go through all the details. Yeah. No, tell us about the sales life cycle. So what are the kinds of deals you're not talking about rubber chicken right now?

We've got a panel. But yeah, tell us about the sales life cycle. The juice has been really good, by the way. Thank you. Yeah. So the life cycle, it's been, it's been, it's been wild. So, you know, Gecko actually became profitable in 2017, right after YC's launch in 2016.

And so the- You were a YC company? Yes, 2016. And what was interesting about that was, you know, we decided to build a company very forward deployed. So instead of building robots in labs, actually funny story, one of the VCs you had here last year offered a bunch of money at YC for us not to leave and go back to Pittsburgh and do this forward deployed motion of building robots, but instead build it in a lab.

And I turned that down because I just fundamentally didn't believe in that way of building. And so, but, so we decided to launch into the, and build robots, like literally soldered in these environments before, and just figure out how to make the robots work in reality, in the real world.

And so the sales motion was basically, we would go to the plant managers. Sometimes I'd call and be like, Hey, my pizza guy, like, you know, where's the, where's the plant manager? Can I talk to him? And I'd get, figure out how to get to the plant managers. And then I'd convince them to let us work with them in their facilities.

And so started out that way by selling to the folks who need this the most. Yeah. And so, but now I'm talking to obviously CTOs and CFOs because our products are actually very financial and helps with depreciation models. It helps with optimizations, but we started by just selling to the folks on the ground and building the robot by failing a bunch of times there and fixing it live.

But now we have a great platform. And so now when customers buy Gekko, the only way they can buy it is through software. So they buy cantilever and they bought it, they buy an implementation of the software, which is the robots getting the data. And then they pay for a license for the software.

And we try to make data refreshes, which is basically robots going out and collecting more information free. Is there a custom deployment in every one of these? Because they've all got to have different facilities and how hard is it to kind of customize or you have standard standardization now in each deployment to kind of do a Chinese menu type selection?

Yeah. We try, so it's a great question. We started in the beginning by letting the customers pick what kinds of data layers they want. So data layers basically mean what kind of robots. Now we actually don't allow them to do that. We follow all the standards, whatever like API, which is like these governing bodies about how to take care of infrastructure.

But then we go way beyond that because I want to create an incredible user experience that they cannot revert back from. Jake, there's all kinds of crippling infrastructure problems around the world that are not necessarily tied to some of the obvious industries like oil and gas. So I'll give you two examples.

One was what happened in Baltimore where, you know, this, who knows how it happened, but basically the bridge just collapsed in a situation that, and maybe it was supposed to be, and it did. Another example was a few years ago in Genoa, in Italy, an entire slab of a bridge just collapsed and it fell on top of an environment and killed a bunch of innocent civilians.

So there's, I think, a public safety requirement here, which is like some of this stuff was either designed poorly or designed very quickly. How much of that is observable by these kinds of robots? And how do you convince folks that beyond depreciation and financial motivations, there's a, you know, a real need to make sure that this public infrastructure is safe and you guys can secure it?

Um, great question. So the answer to, uh, of how can we actually get information on those types of instances? Yes. Um, we, we, like you can look at a concrete bridge and say, Hey, there's some decay here, or there's something that's happening in the girding here. And you can recognize that and learn and be able to say, wait a minute, you need to send inspectors or shut the bridge down or stop and figure this out.

You first want to do, so what the robots are really good at is getting a crap ton of data about the assets. And then you can pinpoint exactly where to put fixed sensors in specific locations that will be indicative, but then also because of our, of our, you know, we have this like really interesting data set that tells us because of so many different types of situations, what kinds of potential issues are occurring that we can extrapolate out to these types of situations that we might be not as familiar with.

So we'll put fixed sensors on to give us indications and give us some ability to help prioritize spending. And so, um, you know, we just actually signed a contract with governor Shapiro to do this for bridges in Allegheny County and in Pennsylvania, where Pittsburgh is. And, um, we're helping to modernize bridge maintenance and prioritization of budget because what you can see here is you don't necessarily need to rebuild stuff.

And in some ways that's not even practical, but you can figure out where to deploy capital. Um, and then, by the way, did you see this video on X where somebody was going through the Lincoln tunnel and it looked like it was about to burst? There was like water creaking in and it was really disconcerting.

Yeah. Um, but I think it was more of a design feature to actually like alleviate when times when the water levels were super high. But my point is there's all of this stuff that we interact with that it would be good to know that there's a, you know, a service out there looking for it.

And is, is there a world where you could also then theoretically ingest like the actual architectural or CAD of these things and then also be able to do diffs and variants and be able to tell people, Hey, hold on a second. This is not conforming to how we thought it should be behaving.

Yes. We do. We do pull those in as much as we can, but it's important to remember that most of the infrastructure that I'm talking about is like 60 years old. Um, now on the new build side, like for the new Columbia class submarines, for example, um, there's an issue where like, there's not a digital thread.

You have like 5,000 different contractors that are trying to make us most powerful sub in the world and they're handing paper to each other, um, as they build the submarine. And so it causes one to be a bunch of delays and issues, which we're seeing with a lot of our ability, like China, for example, can outbuild us by 232 times, uh, submarines that is, or, or new ships.

And a big part of like art are like, we have to be able to figure out how to be smarter when we manufacture. And so one of the ways you can do that is digital threads all the way through the manufacturing process so that we're not like delayed by handing paper to each other that may or may not be incorrect.

And for the customers that we work with, you know, most of them, you know, you're looking at drawings that are 60 years old. They have never been converted. Um, I, we even try to get asset lists from customers and they're like, we don't have it. So we have to go out and actually build that for them.

How should we build a submarine just off topic? How should we build it? Yeah. So we don't have 5,000 contractors at 0.1% or 0.2% the speed of China. It's a good, it's a good question. I think we should orient to a most efficient, um, way of building as many components in one place as we can.

But you have to remember as well, you know, congressional members have their own constituents to advocate for. And so they want to bring jobs to their, to the local communities. And so in a, in a democracy, it's really tough actually. Mm-hmm. Jake, how do you see, um, where your bots, your robots, and let's say the more traditional generalized humanoid robots intersect and when they meet, how do you think about that problem?

Oh, it's a great question. Um, I am so excited to buy as many Optimus robots as possible. Yeah. You're a customer. You'll be a customer. A hundred percent. You're not going to be a competitor? No, no, no. I mean, um, look at any botics right here. So this is a, this is a sweet, it's a company based in Switzerland that have an incredible robot and their data doesn't know where to go.

And so the, the idea of robots, this is what I firmly believe is that- Zach's taking them home today. Is that, um, you know, they get sensors to places that are really hard to get sensors to. And so that information has to be funneled, um, somewhere to drive some large business outcome.

So I really don't think like, you know, I am not of the belief, I guess, that when Elon talks about, you know, two times the amount of, um, robots as humans, that you'll see them in society actually, as much as you may, maybe you'd think, I think they're actually gonna be found mostly in these, like, really dangerous, behind the scenes, industrial settings, which I, in my opinion, that's like where they should start for sure, because you'll have to, you know, one, you have to, those are really complex tasks, but two, they're like very beneficial for humanity.

Um, so like, is there, is there a generalized platform that you've built that allows you to solve for these different use cases, or do you find that there's a lot of application specific engineering that's required? Good question. So we, we, we built an API platform for robots where companies can try their systems out and we can, because we have a go to market, we can now test if that robot is producing something valuable from a data side.

Right. And I'm not actually as interested in robots that can weld or robots that can clean right now. Mostly I'm just interested in just like, what kind of information and data can we build better operating platforms and systems on? And, um, so that's, that's where I'm starting. And we'll begin to add more robots that can do different kinds of jobs.

But, um, you know, I think it's, you have, I think this is where first ordered data sets and, and, and software companies have become more and more, you know, powerful. This is why maybe, you know, um, my opinion on the, the standalone SaaS model is like, I think it's going away.

Um, because the companies that are so advantaged with first order data, you can, you know, just build your own software. have a capital markets last question of capital markets kind of embraced the story or things you can invest. Okay. Yeah. Um, yeah, they, they really are. I think, um, cause there's a lot of this like hesitation around deep tech and hardware historically, but you've obviously got an incredible software layer and great recurring business.

So you seem to be pretty differentiated in terms of all, a lot of the long haul build cycles that I think we see out there. And, uh, yeah, and hardware in our case is very sticky. Yeah. Because, you know, once you convert from, you know, paper and you're now not using binders, you're using cantilever.

Yeah. Um, it's, it's really hard to go back. Yeah. I just, yeah, I think it's, we've talked before and I think what you're doing is so inspiring because it feels to me like this technology, you know, that we've seen over the last 20 years in iPhones in EVs and sensors, um, is now allowing us to move from being reactive and trying to figure out what happened to then saying, Hey, let's be proactive.

What are the opportunities here to extend life, to sit, you know, extend the life of these assets, um, and avoid tragedies. And I just, you know, I think the work you're doing is a real grinders work, but it's going to save lives. And it's going to really save taxpayers money that can be deployed in other places for beautiful things.

And so I just want to commend you on doing something that is so essential. Um, in many times I meet a founder and they are doing something and I just think, God, I, I don't know if this is going to work or not, but I know that this founder is going to figure out a way to make it work.

And I think it's just really rare that somebody cares so much about something and then executes as hard as you have. Um, and I just want to tell you, I personally very much appreciate it because you know, when a bridge collapses, I know the one in Italy, it's, it was a very big tragedy.

I think it killed 50 odd people and it was privately owned. Yeah. And there's a, you know, very, very wealthy family that owned it. And ultimately, sadly, no repercussions. Yeah. Same with dams in Brazil, same with dams in Brazil. Right. So, you know, as public infrastructure becomes private infrastructure, then the profit motive supersedes the safety motive.

you're going to get all these things unless there's, um, um, some sort of check and balance. So long way of saying we very much appreciate this hard work that you've done. Last question. Yeah. You're, you're, you know, you're, you've been in the YC community for a long time. Just really, you don't have to say yes or no, but have you ever been forced to do any founder mode?

Did you feel pressure? Have you just, just blink twice, Jake, just tell us the truth. Jake, thank you for joining us. That's awesome. That was awesome. That was excellent.