Jake Loosararian，Gecko Robotics

Speaker 1 (00:00:00): Gecko Robotics builds wall climbing robots and enterprise software to maintain and protect essential infrastructure. Speaker 2 (00:00:07): He is the CEO and cofounder at Gecko Robotics. Jake, thanks for joining me today. Jake Loosararian (00:00:12): Every hour you save, you're saving potentially 1,000,000 of dollars. Jason Calacanis (00:00:16): Hardware is hard. Be very careful. Jake Loosararian (00:00:19): There's a lot of really important problems to solve. There's so much sex appeal to building new things. But, yeah, I get the business model right. And the business model has to make a CEO or a CFO give them. Speaker 2 (00:00:31): There's a huge fire going on right now at Philadelphia Energy Solutions. Oh my gosh. Again, look at Speaker 1 (00:00:36): this, guys. Look at this video Speaker 2 (00:00:37): right now. Today, the Navy remains a formidable video right now. Today, the navy remains a formidable fighting force, and even officers within the service have questioned its readiness. At a missile silo we visited, time and frigid weather had clearly taken their toll. Chamath Palihapitiya (00:00:52): Developing right now, gushing for hours with no end in sight. Thousands of barrels of crude oil spilling from a tank. Speaker 6 (00:01:00): The report does an estimate of what the need is to bring the overall grade up to a b, which which is what the society sort of determines to be adequate, and it's like $4,590,000,000,000. Chamath Palihapitiya (00:02:02): Yeah. Alright. Hi. Jake Loosararian (00:02:05): I'm Jake, the founder and CEO of GEC Robotics, a company that makes robots and software to help diagnose the health of the built world. Now, what 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 6. See, maintaining things has always been an afterthought. But that afterthought is now a $4,590,000,000,000 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 $400,000,000,000 on maintenance, not on building new things, just keeping old things working. And Fortune 500 companies will lose $1,500,000,000,000 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 is on a rope. Now, Joe is armed with a handheld sensor and what looks like an excruciating wedgie. Now, Joe is 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. Jake Loosararian (00:04:25): 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 headfirst. No. I I actually dove in headfirst straight to this hole and when you got through this hole, you got into a 200 foot tall steel tube 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 him $2,000,000 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. Now, 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, $30,000,000 just that year. And I became absolutely obsessed with how we understand the health of the built structures that we use every single day. And so I started a company. And I boot shopped that company for 3 years, pouring my life savings into it. I slept on my best friend's apartment floor and I was down to the last $100. Two things happened within 2 weeks. Jake Loosararian (00:06:21): 1st, I got an offer to buy the company for a company that makes power plants. And then second, 2 partners from a group called Y Combinator said that if I stayed poor and kept on building the vision, that one day GECO would change everything that we knew about built structures. So I decided to stay poor and keep building the vision. 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 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 moat, 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 moat, 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 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 themselves. 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 Kaleiber, 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. Jake Loosararian (00:08:30): 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 a producer is 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 me and you use in the bathroom every single day. Now, this facility has 1,000 of assets and 1,000,000,000 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, then 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. If you 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 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 Anybodyx, 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, nuclear facilities and beyond. Jake Loosararian (00:10:40): 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've sent 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 tanker leaking into rivers. And once we've gotten this and the customer is really excited because we knew 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 an 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 1,000,000 of dollars. And we can do this in a way as well that ensures 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 510 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 $8,000,000, 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, we actually 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. Jake Loosararian (00:12:54): 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 5% more while only having to incur, over 90 days, an accelerated, an accelerated damage to the asset of about 2 months, equivalent to 2 months. And so we've proved that we could do that by actually lowering the fill heights in the tank and increasing the acid 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 our ability to extend your depreciation models. And then we created a $105,000,000, of value by being able to reduce safety risks as well as environmental, as well as being able to reduce the amount of CapEx that customers needed to spend. And the estimated from the customer was a 4% 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 a 100,000 tanks and that we could provide a $122,000 of ROI per tank. Now initially, we signed a $30,000,000 contract. It's exciting. It's gonna extend to a 100,000,000, but it shows how if you adopt technology in a way like this, it's unfair. Jake Loosararian (00:14:55): Now, on the defense side, we're working with the, Air Force on a $130,000,000,000 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, he 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 different areas trying to gather information and data sets. We improved that to be able to reduce labor by 85% 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 manufacturing sector, that's where most of our 7 to 8 figure accounts lie with big contracts like Exxon, BP and 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 US, you can reduce emissions by about 18% if you can stop those kinds of things from happening. So technology is available today to make a drastic impact on net zero. Jake Loosararian (00:16:48): And then it turns out as well, if you're the best in the world at 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 a $132,000,000,000 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. Now this is why it matters. You see, I'm not crazy. Building robotics, material science, a I, software, a 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 years years, and it's no wonder why. They're building their foundational models off of Jove'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 20 years in software. And my journey through the Rust has given me both 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 a I for the built world with systems like Cantilever. Thank David Friedberg (00:18:52): you. Thank you. That was awesome. Yeah. You're a puppy. David Tuck showed up, everybody. Doggy. Hey. Sacks, that's a robotic dog. It's your puppy. Jake Loosararian (00:19:06): Your kinda dog. Speaker 1 (00:19:07): Woah. Jake Loosararian (00:19:09): Go to Sacks. Go to Sacks. Go give him a kiss. Oh, there we go. Saks is very affectionate, so No. You can pet him if you want, David. David Friedberg (00:19:19): You can pet him, Saks. Chamath Palihapitiya (00:19:21): Here. Where where do you pet him exactly? Jake Loosararian (00:19:23): There you go. Speaker 1 (00:19:25): That was Jake Loosararian (00:19:26): a lot of love. Jason Calacanis (00:19:26): Yeah. I am experiencing Jake Loosararian (00:19:28): Oh, wow. Jason Calacanis (00:19:29): Companionship from this Jake Loosararian (00:19:31): dog. It's exciting. Jason Calacanis (00:19:33): Yeah. It's definitely it's definitely a nice dog. Yeah. Nice dog. Doesn't Chamath Palihapitiya (00:19:40): bite. Jake, good boy. David Friedberg (00:19:42): I think one thing that would be great, based on the kind 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 physical work life, it's impossible. Chamath Palihapitiya (00:20:01): I didn't know where to sit. David Friedberg (00:20:02): Were you worried about the dog? Chamath Palihapitiya (00:20:03): No. This was it's a very poorly organized conference. Jake Loosararian (00:20:06): Oh, yeah. Yeah. Let's talk about that. David Friedberg (00:20:08): Yeah. We we only told you where to sit 5 times in the last 4 minutes, but Chamath Palihapitiya (00:20:12): I don't understand these images. I have, you know Literally, actually, Jason Calacanis (00:20:15): they go in there after, and, like, Sergei comes. He does his first thing, and I'm, like, sir, oh, yeah. Do you have any food? I go out to the food, and it's just like rubber conference chicken in the VIP speaker area. And I'm like, Freyberg, can we just get some sushi from Nobu? David Friedberg (00:20:28): It was all good. Chamath Palihapitiya (00:20:29): We should Jake Loosararian (00:20:29): go through all the details. Yeah. No. I thought it David Friedberg (00:20:31): was Tell us about the sales life cycle. So what are the kinds of deals you Not talking about rubber chicken right now. We've got a panel. But, yeah, tell us about the sales life cycle. Jake Loosararian (00:20:39): Juice has been really good, brother. Chamath Palihapitiya (00:20:41): Thank you. Jake Loosararian (00:20:41): Thank you. Yeah. So so the life cycle, it's been it's been it's been wild. So, you know, Gekko actually became profitable in 2017, right after YC's launch in 2016. And so You David Friedberg (00:20:50): were a YC company? Jake Loosararian (00:20:51): Yes, 2016. And, what was interesting about that was, you know, we decided to build the company very forward deployed. So instead of building robots and 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 believe in that way of building. And so we decided to launch 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, where's the plant manager? Can I talk to him? And I'd figure out how to get to the plant managers and then I'd convince them to let us work with them and their facilities. And so I started out that way, by selling to the folks who need this the most. Yeah. And so now I'm talking to obviously CTO's and CFO's because our products are actually very financial. It 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 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. David Friedberg (00:22:31): 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 do you have standard standardization now in each deployment? Chamath Palihapitiya (00:22:41): Do they David Friedberg (00:22:42): kind of do a Chinese menu type selection? Jake Loosararian (00:22:44): We try so so it's a great question. We we started in the beginning by letting the customers pick what kinds of data layers they want what 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 your infrastructure. But then we go way beyond that. Because I wanna create an incredible user experience that they cannot revert back from. Chamath Palihapitiya (00:23:08): And Yeah. There's, 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. 1 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 and it did. Another example is 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? Jake Loosararian (00:24:05): Great question. So the answer to of how can we actually get information on those types of instances? Yes. We we Chamath Palihapitiya (00:24:13): 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. Jake Loosararian (00:24:28): You first wanna do so what the robot's 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 We have this 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 an ability to help prioritize spending. And so, we just actually signed a contract with Governor Shapiro to do this for bridges in Allegheny County in Pennsylvania, where Pittsburgh is. And, 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, and then Did Chamath Palihapitiya (00:25:25): you see this video on x where somebody was going through the Lincoln Tunnel, and it looked like it was about to burst? Yeah. There was, like, water creaking in. Really? It was really disconcerting. Jake Loosararian (00:25:35): Yeah. Chamath Palihapitiya (00:25:36): 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 my point is there's all of this stuff that we interact with that it would be good to know that there's, 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? Jake Loosararian (00:26:04): 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. Now on the new build side, like, for the new Columbia class submarines, for example, there's an issue where there's not a digital thread. You have like 5,000 different contractors that are trying to make the most powerful sub in the world. And they're handing paper to each other as they build the submarine. And so it causes 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, submarines that is, or new ships. And a big part of our, we have to be able to figure out how to be smarter when we manufacture. 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. 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. David Friedberg (00:27:07): How should we build a submarine? Just off topic. Chamath Palihapitiya (00:27:10): How should we build it? David Friedberg (00:27:11): Yeah. So we don't have 5,000 contractors at Well, it point 1 percent or point 2 percent the speed of China or Jake Loosararian (00:27:16): It's a Chamath Palihapitiya (00:27:17): good it's Jake Loosararian (00:27:17): a good question. I think we should orient to a most efficient, 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. David Friedberg (00:27:33): Mhmm. Chamath Palihapitiya (00:27:34): Jake, how do you see where your bots, your robots and, let's say, the more traditional generalized humanoid robots intersect when they meet? How do you think about that problem? Jake Loosararian (00:27:45): Oh, it's a great question. I am so excited to buy as many Optimus robots as possible. Yeah. Chamath Palihapitiya (00:27:51): You're a customer. You'll be a customer. 100%. David Friedberg (00:27:53): Yeah. You're going to be a competitor? Jake Loosararian (00:27:55): No, no, no. I mean, look at Anybodyx right here. So this is a company based in Switzerland that have an incredible robot, and their data doesn't know where to go. And so the idea of robots, this is what I firmly believe So David Friedberg (00:28:09): what's taking them home today? Jake Loosararian (00:28:11): Is that, you know, they get sensors to places that are really hard to get sensors to. And so that information has to be funneled 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, 2 times the amount of robots as humans, that you'll see them in society actually as much as maybe you'd think. I think they're actually going to be found mostly in these, like, really dangerous Behind Chamath Palihapitiya (00:28:37): the scenes. Yeah. 100% industrial settings. Jake Loosararian (00:28:40): Which I in my opinion, that's like where they should start, for sure. Because you'll have to 1, those are really complex tasks. But 2, they're very beneficial for humanity. Chamath Palihapitiya (00:28:50): 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? Jake Loosararian (00:29:00): Good question. So 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. Chamath Palihapitiya (00:29:13): Right. Jake Loosararian (00:29:14): 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 what kind of information and data can we build better operating platforms and systems on. And so that's where I'm starting. And we'll begin to add more robots that can do different kinds of jobs. But you know, I think this is where first order data sets and software companies have become more and more powerful. This is why maybe, you know, my opinion on the stand alone SaaS model is like, I think it's going away. Because the companies that are so advantaged with first order data, you can, you know, just build your own software. David Friedberg (00:29:52): Have, capital markets last question. Have capital markets kind of embraced the story or things? You can invest. K. Jake Loosararian (00:29:59): Yeah. Yeah. They they really are. I think, David Friedberg (00:30:03): because 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 a lot of the long haul build cycles that I think we see out there. And, I got Yeah. Jake Loosararian (00:30:20): And and hardware, in our case, is very sticky. Yeah. Because, you know, once you convert from Right. You know, paper and you're now not using binders, you're using cantilever Yeah. It's it's really hard to go back. Jason Calacanis (00:30:31): Yeah. I just yeah. I think it's we 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, in sensors, 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, and avoid tragedies? And I just you know, I I think the work you're doing is a real grinder's work, but it's gonna save lives, and it's gonna really save taxpayers' money that can be deployed in other places for beautiful things. And so I just wanna commend you on doing something that is so essential. In many times, I meet a founder, and they are doing something, and I just think, god, Jake Loosararian (00:31:21): I Jason Calacanis (00:31:21): I don't know if this is gonna work or not, but I know that this founder is gonna figure out a way to make it work. And I I think it's just really rare that somebody cares so much about something, and then executes as hard as you have. And I I just wanna tell you, I personally very much appreciate it because, you know, when a bridge collapses I know the one in Italy. It's it it was a very big tragedy. Chamath Palihapitiya (00:31:41): 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. Jake Loosararian (00:31:51): Yeah. Same with dams in Brazil. Chamath Palihapitiya (00:31:53): 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 gonna get all these things unless there's, some sort of check and balance. Jason Calacanis (00:32:04): Yeah. So long way of saying, we very much appreciate this hard work that you've done. Oh, last question. Chamath Palihapitiya (00:32:08): Yep. 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? Jason Calacanis (00:32:19): Did you feel pressure? Chamath Palihapitiya (00:32:20): Have you just just blink twice, Jake. Just tell us the truth. Jake, thank you for joining us. Yes. Jake. Speaker 1 (00:32:29): That's awesome. That's awesome. David Friedberg (00:32:31): That was excellent.