Inside Zipline's Autonomous System: 140M Miles, Zero Incidents

I remember being in Rwanda early days and going out and meeting with some of the doctors and lab techs that we were serving and asking for them like, you know, how's it going? What do you think? What's your feedback? Here I am kind of, you know, up and coming, you know, learning engineer thinking they're going to say something about the drone or some of these things. And the main piece of feedback that I received was people get sick 24/7. Why are you guys only open 12 hours a day? Hmm, right. Especially when you're delivering my saving blood. Yeah, exactly. And so that was a really key insight for me where it's like, man, we've found product market fit in a market where yeah, our, you know, our product wasn't great yet, but it was solving a real need. And so having that really be tried market where there's a real problem being solved and when your customer is telling you that their main feedback is they want more of your service, that's a good sign. Welcome, Keller and Eric to the show. You guys have been working at zipline for a long period of time. Keller is the co-founder and Eric, you are in charge of systems engineering and safety. And we got lots of things to talk about in this whole world of drones, drone systems. And how you guys started in this hardware space before LLMs even started. So we have lots of questions. Awesome. But you don't like zipline being described as a drone company, even though you're probably the largest autonomous drone company in the world right now. I mean, you know, we we've always wanted to be an extremely customer obsessed company. And the reality is none of our customers care at all about drones. You know, like we our goal was always to build an automated logistics system for earth and to approximate teleportation. And all the customers who are like living on zipline today, they really don't care how they don't care about the technology operating behind the curtain. What they care about is their ability to like download an app, open it up, you know, see a huge number of different brands and amazing restaurants that they want to shop with and then click a button and have it delivered to them five minutes later. So we've always really tried to focus on the experience rather on rather than on like this specific technology. Well, this shows about technology. We're excited about that technology. What is the underlying technology behind zipline? You started in 2011, you pivoted in 2014. This is way before anything related to AI robotics, foundation models, anything related to that. Yeah. But you've kind of you were before all of that and you're writing the wave of all of the things that have come afterward as well. Yeah, this was when like starting a robotics company was the dumbest thing you could possibly do like why you know you're talking to an investor in that time about. I mean, it wasn't easy and it was particularly hard because so many of those conversations, you know, I mean, I was 23 24 Eric joined the company around that time. And we were starting to describe this vision of autonomous logistics system for earth, there would be 10 times as fast, half the cost zero emission. You know, one of the biggest problems when we're trying to raise money for that vision was investors would say isn't this illegal in the US. In fact, I think that that's a question you asked me when we started talking about this. We weren't allowed to fly beyond visual light of sight. I mean, we weren't allowed to fly at all really, but like, yeah. And so the answer was yes, it's illegal. And then most investors would be like, well, we don't invest in illegal things. So like we're not going to invest. But you know, for weird reasons, this is what basically took zipline down this path of like, well, if it's illegal in the US, then we can launch in other parts of the world. You know, the value of the service would be extremely high. Is it playing decided to launch in Rwanda in 2016 delivering blood transfusions directly to hospitals and primary care facilities. This enabled us to have a use case that was so powerful that a government would work very closely with us to make it happen. Make it legal and to make it legal or at least make an exemption to their existing kind of regulatory framework. And, you know, and then the other thing, you know, when it comes to how to think about zipline as a company, you know, when we launched in 2016, we were like, we have this really cool drone. We put all this work into designing this really cool aircraft that, you know, and it's, it has all these great fundamental features. And when we launched, it was a total disaster because the reality, what we, what we learned in that first year is for the first eight. The contract is signed 21 to serve 21 hospitals and we serve one hospital for the first nine months and Eric in particular, like how much did you sleep during those. I mean, to a lot of time in Rwanda and sleep a lot. And then when you're in the US, like, we'd get woken up at like midnight because that's when the distribution center was turning on and it would everything would be broken. Nothing was working. It was totally desperate constant all nighters and working through the weekends because we made this big error, which was the thinking that like the cool vehicle was the majority of the solution. What we learned during that first year is that the drone is 15% of the complexity of the solution. This whole drone, the hardware of it is only 15%. We had to build so many auxiliary software systems, maintenance systems. How do we hold the inventory and do inventory management? How do we integrate with a national civil aviation authority, which we'll talk more about? How do we integrate with a national healthcare system? How do we do ordering and demand management? We had to build out all of these other parts of the overall logistics system. This is the reason I think a lot of people might look at Zipland and be like, wow, it's a cool drone company. They build a cool aircraft. The reality is the aircraft is like 15% of the solution that's required to build something that just feels like magical, reliable teleportation 24/7 365 to the now, you know, hundreds and millions of people who depend on the service. Speaking of 24/7, I remember being in Rwanda early days and going out and meeting with some of the doctors. And lab techs that we were serving and asking for them, you know, how's it going? What do you think? Just really being customer obsessed and wanting to optimize the product. And, you know, here I am kind of, you know, up and coming, you know, learning engineer thinking they're going to say something about the drone or some of these things. Right? So, especially when you're delivering my saving blood. Yeah, exactly. And so that was our, you know, you got to start somewhere, right? So we started being open 12 hours a day and trying to expand and grow from there. And so that was a really key insight for me, where it's like, man, we've found product market fit in a market where yeah, our product wasn't great yet, but it was solving a real need. And so having that really be tried market where there's a real problem being solved. And when your customer is telling you that their main feedback is they want more of your service, it's like, that's a good sign. Yeah, we were 24/7 within the first year. So we went 24/7. We're now 24/7 365. I mean, on Christmas day, I usually call all of our different distribution centers to like, thank them and check in with them. So like, there is no day when these facilities don't depend on, you know, we went from serving one to 20 to 500 now to 5,000 hospitals and help facilities across the world across eight countries that are served by the system. It's become the largest commercial autonomous system on earth. And besides that for us, the largest system on earth just crossed 140 million commercial autonomous miles, which I mean, how many times did that? I think that that's like, did the sun and back or one of the one of the things that I like is every road in the United States. There's a lot of roads in the United States driving on every single road more than 30 times. Wow, it's good set. That's a lot. That's a lot just to put in perspective. You know, seeing the impact that that system is now having in across all these eight countries. I mean, University of Pennsylvania just published a study showing a 51% reduction in maternal mortality. Thanks to zipline. So half as many moms losing their lives in childbirth. We have, you know, across all the different use cases is it blind serves some of our partners estimate that we're saving between 10 and 12,000 lives a year. And that impact is growing exponentially as we're now expanding, especially as a result of this new partnership we have with the U.S. State Department. What is a partnership in December? We announced a $550 million partnership with the U.S. State Department to expand the impact of zipline's life saving service across a lot of the countries where we're already operating. So with USA being shut down, the U.S. was really seeking like new ways of engaging in these countries and helping save lives in these countries, but they wanted to do it in a way that would. That would accelerate the economies of these countries and help the U.S. economically. And so the new strategy they're calling commercial diplomacy. The idea is that we want all of the developing world should be built on top of US AI and robotics technology. We should be going and, you know, economically helping we should be bringing the best that the U.S. has to offer. The biggest thing is when you talk to these countries about what they want, they'll tell you they are sick of low quality aid provided by NGOs for free because these services and gender dependence and prevent economic growth in the countries. What they want is high paying jobs, entrepreneurship technology. And so the U.S. is going through a big strategic shift where it's like, well, we have that. We have those things. So let's basically go out and incentivize these countries to adopt that kind of infrastructure, make sure that as these countries are accelerating, they're doing it using US robotics and AI technology. This is something that will be great for those countries, it saves lives, it saves them money, but it also means that it will make it possible for the U.S. to secure our lead in manufacturing and robotics of the decade to come. I'm curious about, you know, you guys, because you now run the largest autonomous system in the world. And you, you watched it 10 years ago at this point, so you've been in production for 10 years, you've learned a lot of stuff that your average engineer sitting behind a computer screen has no idea they're going to run into when they try to deploy AI into the real world. So I'm curious what some of those lessons learned are. And maybe one of them. One way to ask the question is, what popped up over the last 10 years that you never would have guessed you needed to be good at when you first started launching these systems in 2016? Yeah. You know, we started off delivering life-saving products, right? And our customers need, need life-saving products all the time in all weather conditions. And you would think it's, you know, wind these things, but one of the weirdest things is actually solar weather. So there's solar flares that happen on the sun. So basically, big explosions that send radiation to the earth, they can mess with the ionosphere, and that can cause basically the RF signals coming from GPS satellites to be faster, slower than you expect. And that can lead to degradation and challenges in navigation systems. And so here's one example that, you know, when we were starting off, we didn't think that this was going to be something we have to figure out. But we actually have, you know, gone pretty deep in this space, and really it's two things. One is designing our navigation system in our GNSS systems to be robust to these conditions, to ensure that we can still know where, you know, where it comes to our, with centimeter level precision in those conditions, in those challenging solar flare times, as well as designing the system to have redundancy beyond GNSS, such that if things get really bad, we can still safely operate. Eric, you're in charge of safety. Tell us about what you've learned about safety today, and specifically about the Compute Failover system that you have. Yeah, yeah, absolutely. I mean, there's so many things that we've learned over the last decade of operating, you know, the system in the real world. One of the things that we're proud of is how we've developed to your point, Compute Failover. So there's a flight computer flies the aircraft. Lots of sensors come into this, into this computer, and that basically does a lot of math. And sends commands to actuators, right? So motors, control services, these things. So this is the brain that flies the aircraft, right? They're, you know, one of the things that we've learned is you need to assume that any part of the system can have a fault, can have a hiccup, something can go wrong, and that's how you really design something to be robust, reliable, and safe. So what do we do if this flight computer has a challenge? It could be a software challenge, it could be a connector challenge, it could be these different things. Bitflip due to solar radiation. All kinds of things, right? And so what we've done is we have two flight computers. And both of these flight computers think that they're flying the aircraft at any given point in time. They all are receiving all the information from the sensors. They're all sending commands to the actuators. And there's like a kind of a third arbiter, a little computer, that is monitoring the health of those two and telling everyone every other node on the, on the aircraft, who to listen to, who's actually in charge? What if the arbiter fails? Yeah. If the arbiter fails, then the primary computer that was flying just keeps flying, right? So one's in charge, and if the thing that's monitoring itself fails, then now we say, okay, like, you know, now we're just going to keep flying on the thing that was good, and we're going to keep flying the mission. So two heads about in one. Yeah. Yeah. So something we're really proud of. We had actually had one of these events happen a couple of weeks ago, yeah, where we had after a delivery, we delivered the package to the customer, and then we had a hiccup on the main flight computer. And we switched over to the backup, their cop flew itself home, landed, everything was totally fine. So just, you know, designing the systems to be robust reliable through and through is, you know, how you get the two and a half million deliveries and 140 million miles flown with no safety incidents. And a lot of what zip lines doing, it's not like, oh, this is totally revolutionary. No one has ever thought about having a secondary flight computer. Yeah. That's how Boeing triple seven works. Yeah. But the cost of a flight computer on a Boeing triple seven is in the millions of dollars. And so a lot of what zip lines having to do is take a lot of the best ideas that you can see in aerospace safety best practices and then you've got to figure out how to build that using components coming out of the smartphones supply chain. Yeah. Yeah. You can do it for, you know, tens of dollars or hundreds of dollars. You can achieve similar levels of safety to traditional aerospace, but you can move a hundred times as fast at one one hundredth of the cost. Yeah. So you mentioned that the aircraft is only 15%. To squad the other 85% in like layers and maybe go down deep in some of your systems that are really, really sophisticated. Like you're like, I know this because of being a board member and like the detect and avoid systems. So, you know, how do we test? Why do we test? Really? The way I think about it is there, maybe first of all, we're not a software company, right? We're a real world AI robotics company. And so there's electro mechanical systems out in the real world. So there's hardware test aspects, there's software test aspects, and there's the integrated system test aspects. We have a lot of different environments that we test, a lot of different approaches. I'll name a few of them, you know, on the hardware side, we do a lot of component level testing, halt testing, highly accelerated lifetime testing where we're taking components, maybe it's a motor of these kinds of things. And we're putting them through, you know, through hell, right? We're putting them through all kinds of challenging conditions, making it rain, making it hot, making it human, making it corrosive. All these things while we're exercising, you know, while we're spinning the motor, while we're doing things. All of the things, right? You name it. And just to give a context for a scale, I mean, there are 700 unique components on the aircraft designed from scratch by Zipline. We are designing not just the flight computer from scratch, the power distribution board, the motor controllers, the battery, the battery management system, the, you know, the pod is the smaller robot that we're using to actually make deliveries to people's homes. There's an entire envied, envied GPU powered flight computer on the pod, we're building the electronics that go into the docking station where the zip is flying in and out of, we, you know, all of that, even the electric motor being designed from scratch by Zipline because we need a, you know, a thrust to weight ratio that is not available and off the shell of electric motors. So you have to design something from scratch. So, you know, 700 unique components, 43 major subassemblies on the aircraft, all then coming together on the manufacturing line that you both have gotten to visit and getting assembled into one overall aircraft. But anyway, that's the story. So for each of those components going through this type of testing and, you know, thinking about other industries, oftentimes when I talk to people from maybe automotive or aerospace and some of these, like, hey, how do you think about reliability challenges? And the common answer is like, well, I asked the supplier what the reliability of the part is. Yeah. And I'm like, okay, cool. Like, what if we're the supplier? You know, so, you know, so we're that vertical integration where we have component testing on the ground. We have system testing on the ground where we're taking full aircraft for, you know, and as well as other parts of the, of the system and putting them through vibration tables, wind tunnels, thermal chambers that you can walk into, like, all these things, in order to understand, is, how is this going to break, right? More than just, is it good enough? Like, we want to know how it's going to break. And then we can understand, okay, cool. Like, let's make it better. Or maybe it's like, oh, that's not too worrisome. Like, great. You know, we didn't break any of the ways we were worried about. It broke in that way. Fantastic. Like, we don't just want to say we ran the test campaign and nothing failed. We're done. Like, no, no, let's take this thing to failure. All right. Let's see where the limits are. 49 degrees Celsius, which is very hot, down to negative 25 degrees Celsius, which is very cold. All the things. Yeah. So, you know, flying anywhere at 49 degrees Celsius, you do that's what we would have tested 49 if we're not. Where are you flying at 49? I think Phoenix during the summer. Phoenix during the summer. Yeah. And then where's -25? It's at northern parts, northern parts of United States. Actually, I can't explain. Actually, guys, how you think about that? Like, I could imagine a different version of the world where you guys are like, hey, it was too hot. We're just not going to fly. Totally. And it was too cold. And it was raining too hard. Yeah. And like, there are trade-offs to be made, you know, and obviously your customers would prefer that you fly at all times. That's right. But how do you think about those trade-offs? The easiest way to think about the trade-off was because of the use cases that Zip Line started with. That's right. Which is basically what I should say. You're just - Yeah. You can count on us with your life and the lives of your loved ones as long as the sun is shining. Yeah. Yeah. So basically you develop the capability because you had to initially use cases. Zip Line would fly. And in fact, I mean, for the first, you know, for the first couple of years, we took a lot of risk. We would basically fly. We were like, look, if it's a life-saving delivery happening and there's someone whose life is on the line, we're going to go for it. And we had a situation authority that was, you know, generally a great partner with us on that front. We took a lot of risk. We learned a lot. And, you know, almost always, it worked out in favor of like we saved the person's life. And you know, the worst thing that could happen was we had a parallel land, which is the kind of like Zip Line's safety mechanism of last resort is we can pull a parachute on the aircraft and bring it gently to the ground. Health and the same. But we learned a lot. It happened very often in the first few years, like, yeah, very, very rare today. Yeah. I mean, and put it in a perspective, you know, our original goal was to be 10 times safer than cars. Actually, Alfred was the one pushing in our last board meeting. He's like, that's a BS goal. We need to be two times safer than Waymo. And so Eric literally went and reset the goal, like the Zip Line's target for the end of this year is to be two times safer than Waymo. He's like cars. That's like our technology. Waymo, I think, is about 10x, right? Whether about 10x cars. 10x cars. So our goal is to be two x safer than Waymo. Yeah. And so that's the right comparison. You're flying. You have to be safe in the air, not safe. I think it depends. We're substituting something that's typically going in cars. So it's like debatable. But you know, suffice it is, I mean, we now have 140 million commercial autonomous miles and zero safety incidents. Zero. If you were to drive 140 million miles, you would have 600 accidents, 100 injuries, somewhere between two and six fatalities, depending on what country you're talking about. And you know, this is why it's, you know, we really pride ourselves on like picking the right use cases. It's like, it's life-saving and it really makes a lot of sense to go do it. And also we're going to be, by God, we're going to be as safe as humanly possible from an engineering and testing and validation perspective. We really take that, that's a, that's a deep part of the DNA of the company. One last point. You know, what is the outcome of all of that testing that Eric is talking about, the outcome of all that testing is we have individual aircraft in the commercial fleet that have more than a million commercial autonomous miles and so I think people you know that's just from an intuition perspective a lot of people look at this and they're like wow it kind of seems maybe exquisite or fragile probably very sensitive to like extreme conditions or weather mean you know raise your hand if you have a car that has a million miles on it. It's pretty impressive. These systems are already like way more rugged and durable and robust than people necessarily think. Can I say about the precision like one of the things that blew my mind when I saw some of the I haven't had a chance to experience in person you know I've got to come back. I know I've got a lot of experience. But just just in watching the videos the drone's a hundred feet up and it drops the package it lowers the package to a I don't know a circle that's got a 18 inch radius or whatever it is right like how do you guys achieve such precision even when it's windy even when it's raining yeah how do you pull that off. First of all the aircraft's about 100 meters up. 100 meters yeah so it makes it harder. And you know the multiple layers there's the the delivery pod that comes down yeah right so delivery pod comes down that's really the delivery and pick up like precision part of it right so the the drone is hovering above it you know it knows where the target is maybe let's say it's it's this coffee table for example there wasn't a roof above us so this coffee table and so the aircraft is going to hover above but it actually needs to consider what the wind conditions are yeah right so if the wind's blowing in one direction then the aircraft is going to kind of be shifted you know upwind right so it's going to shift in the direction to help with that with those wind conditions and then it's going to lower that delivery pod down as Keller mentioned we we do take advantage of GNSS so real-time command GNSS that gives you centimeter level confidence of where you are but the thing is we don't know the GPS coordinates of this table right it's not like someone came and surveyed the middle of the table and sent us the coordinates right no one wants to do that so what we have to do is we kind of that we use that to kind of get close right we're like okay here's the backyard here's where we kind of know things the things roughly are and then the job of this delivery pod is to be lowered down you know fight the wind conditions fight these different things and be able to use its onboard perception and autonomy systems to identify where's the best place for me to leave the package right like if there's a little table and there's a whole bunch of drinks probably I shouldn't you know try and drop down on top of these drinks and make a mess maybe I should go to the ground right next to the table right and so it has these autonomous you know onboard real-time compute to be able to identify what am I looking at what am I seeing and how can I find the best place to leave the package and then come down touch the ground opens its doors gets retracted back up and there you go the package is left on the ground and the the delivery pod comes back up stows and their graph flies back home a couple big advantages I mean just to be specific so that pod it not only has its own Nvidia GPU running its own AI autonomy stack so yeah on survey and like no exactly where it's delivering even at night yeah but it's it's also controlling its own position that's right in the x and y axis so it can yeah it can not just know but then move and the advantage of that architecture which you can probably guess but there are two huge advantages of doing it in this way one is it's quiet people have this perception of I mean first of all most drones are really freaking annoying like the sound is just it's basically the most grating annoying sound that you possibly subject to human too and so you know like we zipline has a big team of aerodynamics, aerocoustics and controls experts every part of the vehicle is designed with sound in mind for the vehicle to be as quiet as humanly possible we wanted to be no louder than the sound of like gentle leaves moving in trees and for the when the pod is delivering we're keeping the main aircraft a hundred meters in the air so it's like the thing that is creating noise is really far away that's also a huge benefit from a safety perspective because the only thing that is coming anywhere close to you your family your pets your kids is something that is super cute and safe and it's really like a styrofoam kind of like a cute anthropomorphic styrofoam tub tub how long was the technology tested outside the United States before he came to the United States and was the path to getting into the US now that you're flying in Dallas and delivering packages there we spent eight years I think right about about about eight years I mean depending how you measure it maybe like six six to eight years and then it was I mean we launched in Rwanda in 2016 our commercial service and we really launched the this kind of next generation home delivery service the thing that's now like in sort of insane hyperscaling mode that only launched January of last year so depending on how you measured it you could even say it was like almost nine years and then when you got to the US was it just smooth sailing what was the sort of regulatory path that you have to go through yeah I mean we really started I would see you know like meaningfully engaging with us with you know FA and other regulators in the US around 2020 or so so it doesn't we didn't show up in 2025 and everything was smooth sailing it was really a partnership of working through as you mentioned in kind of 2016 all the stuff was there was no pathways kind of illegal as we as we joked earlier and so yeah so we really was a partnership to identify hey you know we have shared goals right our shared goals are safe and efficient airspace integration and so while we have experience doing that successfully in different countries we can bring some of that experience and we have opinions on how this should work the regulators had opinions on maybe how they thought it should work and so it was a partnership over the course of a couple years to identify what those paths looked like and how we could kind of converge in a line before we were able to execute on that and you had to show your ability to manage all these aircrafts are flying so you wrote systems you built systems yeah I think it's a huge part of you know Keller's mentioning that the drone is only a part of the you know of the overall system the overall complexity what we're really building is an infrastructure layer right we're building infrastructure layer that can enable instant access to products and you don't do that with one aircraft flying from one place to another place you do that with a network of charging locations hundreds of aircraft spread across an area that with the autonomous systems in the in the cloud that can understand where am I having demand where where do I have supply what I have aircraft what's coming up is about to be the dinner rush what's the weather at these different locations how can I kind of self-balance these things as well as how do I efficiently pull like pull in people when needed right so these these aircraft are autonomous they're operating they don't require human intervention through these flights but there are times in which it makes sense to alert a person that hey maybe there's there's an issue here the weather is a little bit uh you know the wind is climbing in this area right so there are humans you know trained aviation professionals that are monitoring our like our network I would call it they're fleet commanders fleet commanders that's right we used to call them pilots you know because when we originally launched in the U.S the first regulatory permission we got was to fly one to one so that meant that we had one pilot sitting in a pilot in remote piloting command yeah who is sitting in an office basically just observing an aircraft do its thing and again you know it's exceedingly rare that a human should ever have to issue any kind of a command to a vehicle but we would have one human watching one aircraft not great for unit economics but as zipline proved out these systems we went from one to one to one to three one to six one to twenty one to forty we're now operating one to one hundred and have plans to go well beyond that man it well one fleet commander so yeah we technically changed the name because I think pilots confusing so we're inspired by Ender's game we now call these this group of this team of people at zipline we call them fleet commanders and it actually says that in the FAA documentation we say fleet commanders shall do the following and yeah they are overseeing a group of a hundred aircraft and to me this is like the exciting cool thing about technology because people think about like well you know what about you know how humans used to solve this problem it's like it's not you know the it's cool how robots enable humans to like up level right like the human is still getting to like strategically manage the system it's just the human is now maintaining and commanding robots rather than like doing the actual work herself now these guys are kind of in hyper scale mode you saw so many problems in the last ten or fifteen years what new problems are you running into yeah I mean what I would say like thematically I mentioned earlier that as you know getting to two and a half million deliveries the you know the oh it only happens every couple years it's like kind of a one in a million chances yeah these things start to matter right we're we're on the path towards a million deliveries every day and if you have a one in a million situation it's going to happen every single day yeah I can't just just to really make that clear so it took zipline from 2014 when we started building the original version of the technology to 2024 to do our first million deliveries was it the end of 2024 maybe it was even early 2025 actually that we did we had done a million deliveries in the cumulative history of the company yeah so it's a almost a decade maybe say about a decade to do a million deliveries zipline is now in the very near future going to be doing a million deliveries a day and so that is definitely humbling it's like wow okay everything about the way we've been solving the problem is gonna break the bar goes way way up and I mean you know one specific example maintenance becomes really hard like you know the scale of the problems the number of vehicles that you're managing in the fleet the cost of a screw up or if some if a certain process is operating in very inefficient ways becomes extremely high and so there's just high degree of criticality for all these systems one interesting point though you know there are a lot of ways that these systems operate that I think people don't yet appreciate the advantages of autonomy one good example is that like system wants to operate 24/7, it does operate 24/7. So I think people are used to like logistics is generally being like, well, here are the hours when humans are driving trucks. That's not how these systems operate. They want to operate 24/7, they can be fully utilized, they can be as happily delivering at 2am and 3am, delivering some things, so it's like ready for you on your doorstep or in your backyard when you wake up at 6am before you go to work, as they are delivering at 2pm. They can deliver in five minutes. They are available 100% of the time. We are soon going to be flying vehicles straight out of our factory in South San Francisco into commercial operation. If you've seen Tesla Model 3s and Model Ys delivering themselves to customers, Zip Line aircraft will fly straight from the factory into operation, huge advantage from a maintenance perspective. That as soon as a vehicle needs to go through some kind of proactive maintenance, it will fly itself to the maintenance depot. So the human can then quickly make, do whatever process necessary and then the vehicle flies itself back into operations. We can also dynamically assign capacity in a metro based on what the system is seeing. There's no like set home for a vehicle. It can go to wherever it's needed. Yeah, I think to your question about, you know, getting to a million a day and what are the new challenges? I think, you know, Kelerhead on some of them, to the previous thought about the drone is only 15% of the problem. Really, it's the way that we currently manufacture aircraft, maintain aircraft, support all these things, you know, troubleshoot problems. Like the way that we do it today isn't going to work when we're at a million deliveries a day. And so there's like, okay, we need better tools. We need better software systems. We need better processes. We need better, you know, all these things. So it's like, you know, Elon talks about designing the machine that builds the machine. And so, you know, this is really one of the things that I see Zip Line tackling over the coming couple years is, we were going to be investing much more in the machines that build and run the machines. I mean, from a scale perspective, I think the largest airline in the U.S. is doing about 5,000 flights a day. Yeah. Zip Line is going to surpass that in the next month. And when we get to a million deliveries a day, Zip Line will be doing like somewhere between, I don't know, 40 and 80 times as many flights in the U.S. and commercial airspace as all other airlines combined. Yeah. And so it's obviously a different cloud. It's completely different class of aircraft. It's a totally different kind of problem. But the reality is when you look at air traffic control, they don't make a distinction. And so there's, there's also when you talk about all the, you know, auxiliary systems that have to be built, there is a huge transformation that's going to have to happen in air traffic control. As we start to realize that, you know, people are really excited about electrification of vehicles. People are excited about autonomous vehicles. Reality is, as those transformations occur, they're going to be 10 times as many autonomous vehicles in the air as there are using these teeny archaic constrained things that we call roads. And so like the sky is a big place, it makes sense to utilize it. You can give earth back to humans. You can make neighborhoods quieter, safer, less pollution, less traffic. You know, you can make huge improvements to earth if we can more effectively utilize the sky. This is going to require huge transformation of how we think about air traffic control in the U.S. and it means that we need to design it with AI and autonomy in mind, rather than the way it was designed, which was in 1950 using, you know, pencils and paper and note cards and like a human looking out trying to watch the airplane. Are you helping the FAA to design it? It's really, yeah, I mean, what needs to happen is, like, collaborative innovation is one way to put it, right? It's like that one company solving this problem for themselves is not going to solve the problem for the industry. And so we are heavily involved in, I mean, first of all, what a key part of the solution we believe is aircraft should be talking to each other. They should be telling each other where they are. They should be automatically detecting that, hey, there's a conflict on the horizon here. And so therefore we're going to, you know, you go up, I go down, right? These kinds of these kinds of things. And our aircraft do that. And we're working with other, kind of, you know, other new entrants into the airspace with autonomous aircraft and Thomas Jones to do the same thing, to make sure that our systems can talk to their systems. And we can all collaborate to make sure it's efficient and safe usage of the airspace. We're also, to your point Alfred, working with regulators, working with standards bodies to take some of these best practice and innovations that we and others have developed and try and make them, you know, broadly accepted and utilized so that we can all collaborate and we can all, you know, safely and efficiently use their space. Because you guys have developed a really, yeah, we were, when we were launching in all these other countries, like we had to build something from scratch. And so we built the thing from scratch. We provided all this software to the Civil Aviation Authority so that they could use it to monitor this entirely new class of autonomous vehicles in the airspace. Interestingly, you know, there are multiple public companies in the United States that build air traffic control software that are worth more than $10 billion, right? So it's like, I often look at that. I mean, I think there are many companies inside Zipline that are likely, if it's like, oh, that's like a public company inside Zipline, which is having to get built from scratch, we're building it because every part of the ecosystem, we sort of had to build from scratch to enable the overall technology to flourish. You know, air traffic control is an interesting, like the more you learn, the more disturbing it is. I mean, we're starting to see the impact, you know, you read about like, you know, a plane crashing into a helicopter and DC a few months ago, you read about like two planes colliding on, I think on a taxi way in an airport where there was a month ago, you're like, wow, why are all these accidents happening? It turns out like 50% of air traffic controllers are over the age of 45, 20% are are about to retire. And nobody is going into air traffic control as a career path right now in the US. And so there's actually a huge labor crisis around these kinds of jobs. And so you have pressure coming from different angles for like transformation is required. We cannot use a system that was designed for airspace for airspace in the 1950s. The labor isn't available to do it even if we wanted to. And also there is this like giant influx of new technology AI and autonomous vehicles that are going to require us to transform how these systems work. So you're a hardware company and a software company. You built you design your operations manufacturing. You design your own parts. You build the own aircraft. You write your own software. You do your own operations. This looks pretty vertically integrated company. Talk about the benefits of like complete vertical integration versus buying component parts or buying component software and putting it all together. And how you get people to come from such different disciplines and domains to see eye to eye and work together collaboratively. Yeah. I mean, I think that interestingly, you know, this is doing it is such an incredible pain in the butt that you would never do it. Like if you, you know, I have this flag over my desk that says we do this not because it is easy, but because we thought that it would be easy. And this is definitely like the definition of zipline, you know, and it's such a pain in the butt. Actually, that it's almost if you look at the history of all these hardware companies, they all try to not do it first. You can look at the roadster, right? They're like, we're going to use a Lotus Elise chassis. We're going to buy the battery pack from a secondary supplier. And we're just going to put the two together and it's going to be awesome. You know, kind of roads are lost a lot of money and wasn't very reliable, right? But like it was it was an important part of getting to the Model S. Zipline, when we started, you know, Eric knows well, we were like buying everything from suppliers. We were like, you know, paying people to design different parts of the system for us or trying to buy off the shelf stuff. And we crashed airplanes. I mean, at test sites and we just crashed and we crashed and we realized, wow, this stuff is like super expensive. And it's also totally unreliable. And so, you know, part by part, you're like, all right, well, like rip that out, we'll design the motor controller from scratch. Okay, rip that out. We're going to have to describe, you know, design the GPS module from the scratch navigation system. So, you know, part by part, you sort of like rip it out. And I think there's a fundamental realization, probably some are the realization that happened that made the Model S possible. It was like, hey, if we want to build a really great specific product in this totally new area of technology, we're going to have to design every single one of these components from scratch to meet the specific requirements of this new area. You know, you might think, oh, like drones. I mean, there are already lots of drones because DJI makes, you know, plastic quadcopters and they make millions of them. And like the US buys 20 million dollar predator aircraft that can fly a bunch of them. The reality is actually both of these systems are very unreliable. And nothing is in a level of like reliability and safety at unit economics that would work for this new industry, the zip line was trying to kind of like pioneer. And so we realized we had to go build like an automotive grade solution. It has to be super reliable. And it has to be extremely cost effective because you're competing against cars and motorcycles, which are actually really cost effective. And we got 100 years to make them reliable and cheap. So you never do it, I think intentionally, maybe just like slowly freak out and through desperation realize like, wow, we got to tear all this shit out and we got to build it all from scratch. The advantage of doing it from scratch is like, is speed and integration. And so, you know, our offices, you guys know, because you've been, but like when you visit zip lines offices, I mean, we are all like absolutely packed into like, you know, start deans into this small building where you have firmware engineers sitting next to mechanical engineers sitting next to autonomy engineers sitting next to, you know, cloud infrastructure sitting next to arrow, arrow acoustics guidance navigation controls systems engineering, manufacturing engine, everything, all everybody in one place. And then our factory is a three minute drive away. And so our team is like on the factory floor working, seeing parts get integrated into the overall system. And then we have our test sites, which are just a short drive away. So you can go to the test sites, watch the vehicles flying, observe how the system is performing, like combining all these things together means that, you know, stuff is always breaking. Stuff's always going wrong as Erica described. The advantage is when the thing goes wrong, we can basically go straight to the person's desk and be like you and I are pulling in all night or tonight. Whereas if you're Boeing and something's going wrong with the battery on the 787, you're like going and suing a supplier and taking two years to try to figure out who's fault it is. And like it's three layers deep in the rat's nest cluster of like how these procurement deals and supply chains work for aerospace is why it's so broken. - Yeah, I think Pat, to your kind of question there about getting these different discipline folks to work together. - Yeah. - Honestly, I think it's quite easy. It's easy when you have set up the way that Keller just mentioned, right? Like, first of all, everyone's rowing in the same direction, well at the same goals. And when you can ground it in reality and it's tangible, then we're all just here to solve the same problems, right? So we actually, with the vertical integration, with having a very diverse team, we actually cut through a lot of the stuff, right? A lot of the things that happen where, oh, that engineer won't tell me what the actual source code does 'cause they said it's IP. And so we don't actually know what the fault detection looks like and you don't have any of that. You just like literally go walk over, sit next to the person's desk and be like, hey, we failed that test. Tell me about how this part of the system works. Oh, cool, pull up the code. Great, let's look through it. Oh, interesting. You're making that assumption. That's not how I designed it, right? Cool, let's get to the bottom of it, right? And so this idea of just rapid collaboration where you're just, you know, the manufacturing team, the operations team, the engineering team, or I'll just like really together is the way to solve these problems. And I have found that it's actually not that hard, right? When you have those ingredients, it actually makes it, you know, makes it pretty fast and efficient. And, you know, too, I mean, Eric saying that it really makes you realize when you build these like complex AI and robotic systems that combine hardware and software, you really appreciate like the deep religious truth of how dumb requirements usually are. Question every requirement, which is, you know, the number one part of like Elon's algorithm, they talk about its basics. Like question every requirement is like, this is like so profoundly and deeply true. You must have every team question every requirement. The requirement is always stupid. When you, and you're like, well, you know, it's both that you go to this team, and that team, you're like, often you have to dig, like two levels deep to realize like this. But questioning every requirement is a fundamental part of like getting through this. And then, you know, the other thing is delete the part. The most reliable part on an aircraft is the part that is not on the aircraft at all because you deleted it in the last design. That part will never fail. And, you know, you take a lot of inspiration from looking at like the Raptor one, Raptor two, Raptor three, I'm sure you've seen, you know, those engines switch each other. And actually a lot of people who come to the factory now and get to see like the EV3 aircraft, you can see the EV2 aircraft, the EV1 aircraft plus like the 10 different hardware versions that we built on the initial, on the, on the first version of Zipin's technology, you were just delete, delete, delete. Like, you know, there's a huge amount of, it's really hard to delete things. It's an active courage. No one wants to delete the thing. You look like an idiot if you delete the thing and then like the system can't perform or doesn't work because you deleted the thing. But like, you know, true confidence in like the physics and the performance of the system enables you to start deleting things. It's a big advantage of having like full integrate full stack integrated control of all of these systems. It makes it possible to question every requirement. It makes it possible to delete parts. - Yeah, I think first principle is thinking is a huge part of that. I remember the platform one aircraft. Early days it had a deployable tail hook 'cause how it landed to have this big hook like a meter long that would come down from the aircraft and we had a line that would catch that and slow the airplane down as it's kind of complicated contraption. And we had this idea that we should be able to move that complexity to the ground systems and have the recovery system, the landing system, more like an aircraft carrier like crab the airplane, right? We can put the actuation on the basically a robot that goes up and grabs the airplane. And we're like, man, that's gonna make the aircraft so much simpler, so much lighter, so much more reliable. We didn't have it working yet. And it was time to build that next generation of the aircraft. And we're like, so we're building these next week. Do we build them with the meter long tail hook or do we delete the tail hook and put the two centimeter long tail hook on the back and bet that we can get this thing working? We got in a room where we're like, delete it, right? Like let's do this thing. And so we're like, from first principles, it should work. We can make it work. We haven't done it yet, but we can do it. And the next couple weeks looked like myself included, a lot of people pulling a lot of late nights, giving that thing working and sure enough, those first aircraft came and we caught them and landed them. So it's a lot of courage, but that like really being grounded in first principles thinking what the tight integrated team is, is how you do that. Is there a version of the future in which instead of delivering life-saving medicine and cheeseburgers, you're delivering human beings? Handing the board member that's the control they're cause. (laughing) I mean, you know, safe, reliable, battle tested. I don't know, seems like if we're gonna liberate ourselves from the tyranny of streets, it's a pretty decent solution. Gosh, I think I agree with you. I think that he's gonna come to you and ask for another billion dollars. (laughing) I think, you know, a couple thoughts. Like one is that, I think Alfred knows I'm measured in the way I answer that question because to be clear, like, you know, building a new infrastructure layer for the planet that can deliver packages is efficiently as the internet moves information is gonna be one of the biggest companies on earth. Like, it's a huge opportunity and we definitely wanna stay like humble and paranoid about how super hard that's gonna be. The level of execution for us to scale, the way we wanna scale over the next couple of years. And, you know, to put into perspective, I described this goal of getting to a million deliveries a day in the very near future. We now have many partners who are at each asking to buy a million deliveries a day of capacity from Zip Line in the last few months. And so, our operating plan has now become our unit of sale. That's a pretty crazy realization. And it's leading us, you know, we had originally built the, we had sized the entire factory to build 20,000 aircraft a year. That was what it was required for a million deliveries a day. Like, all of this is kind of being thrown, we're realizing the market is way bigger. And one thing, you know, when you look at this, you know, it's totally hyperbolic curve that I think I showed you only a few months ago of like, you know, what our total flights have, you know, total daily flight volumes have done over the last 16 months, the level of complexity of all the different systems that are required to basically like stay on that track is quite high. - Yeah. - But, you know, there are five and a half billion instant deliveries being done by humans in the United States every year. And that's where, you know, we're using a 4,000 pound gas company. - It's not really a constant. It's like half an hour. - Exactly. It's good marketing that it's called instant, but yeah, exactly. And, you know, 30 minutes, 45 minutes an hour, you know, a significant percentage of the drivers report eating some of the food that they've delivered in the last month, like more than 50%, they're a significant safety, you know, concerns associated with these kinds of delivery. But, five and a half billion instant deliveries, what we're realizing when you look, you know, Zipline is now at massive scale in Dallas. And we're now launching four more metros in the next four months. When you just look at Dallas, if you were to extend the buying behavior that we're observing from Zipline customers in Dallas to the rest of the United States, there would be 55 billion instant deliveries happening, not five. - Wow. - 55 billion. - Yeah. There's a huge market expansion. I think it's similar to how people looked at Uber when they were launching in San Francisco. And they're like, oh, even if Uber gets to be 33% of the taxy market in San Francisco, it's only going to be a $15 billion company. And obviously what they missed is like, Uber's now 10 times the size of the taxy market. You know, like, if you make something more convenient and less expensive and a better product experience, people are going to consume a lot more of it. We are clearly seeing customer behavior where customers order every day, rather than a couple of times a month. I mean, I met a grandma the other day who's ordered 350 times from Zipline in the last year. She's 80 years old. - Amazing. - Actually, nursing homes are like big Zipline, like they're like big demand centers for Zipline. - They're pretty fun if you're in a nursing home. - It makes sense. And actually, it's funny. People perceive, I think old people as like maybe being, you know, not capable of using technology. They're all like living on their iPhones. You know, like they're probably doom scrolling actually, which is maybe not a good thing. But like, they are very comfortable using like, you know, Apple ID, Apple Pay, or Face ID, Apple Pay, and just ordering and having it delivered directly to them. So there are definitely not enough humans in the United States to do 55 billion deliveries. - Yeah. - The only way we're going to be able to serve this kind of demand is with automated systems. And there's definitely not enough roads. And when you look at, you know, traffic and most of our major cities, you're like, "Oh, can we just like maybe double the number of cars on the road so that we can do way more deliveries?" It obviously doesn't work. We actually need to be taking cars off the roads if we want to like enable human growth and flourishing. And so I think it, you know, this change is inevitable. - So how many flights are you doing a day now? And how many will you do in a month? - So Plenty is now doing almost 5,000 flights a day. And, you know, we're anticipating exiting this year at above 30,000 flights a day. And our goal is to get to a million flights a day as fast as humanly possible, which we expect to achieve in the very near future. Like all of the supply chain, manufacturing, and capacity decisions we're making right now are designed not just to get us to a million deliveries a day, but also accelerate past that. - The things that are interesting to think about on the Unite Economics front is like, whenever we meet hardware companies, and you always talk about like, how much do you think the system is going to cost? And they're always-- like it's going to cost X and you're like, "Cool, it's going to cost 10X." Just so you know, like when you build it, it's going to cost 10X. That's your advice to founders. That's my advice to founders is like for hardware companies, like if I, because you know, I'm like, try to, you know, be a good seed investor and pay it forward and stuff. And like, you're always meeting these founders and I was like, it's going to cost this much. I'm like, cool. It's just like, assuming it's going to cost 10 times that. Like, does it work? And what would you do if it cost 10 times that? And we're speaking from experience. Like, when we launched our system in 2016, we were charging $30 of delivery to deliver a blood transfusion over 80 to 100 miles. And that was like cost comparable. And so we, that's what we signed the contract for. And we thought that we were going to launch a system that cost about $30 of delivery. How much do you think it cost when we launched? We had $300. Yeah, $300 of delivery. And Alfred was surprisingly chill about it. And you know, we were like, all right, we got work to do. And so, you know, the next year, we got it to like 120. And then the next year, we got it to 75. Then the next year, we got it to 40. Then we got it to 28. Then we got it to 18. It's now 12 for the kind of, you know, the long range technology that we operate outside the US. Right now, what's happening this summer is the fully burdened unit economics of these systems is just now in the process of falling below the cost of using cars to deliver things. And so I think it is a cool moment that I think most people don't really realize. It's happening quietly. Like, you're not reading about this in the New York Times or whatever. But, you know, I think that this thing is happening in the next month or two. That is going to have a big impact on the world and how the world looks and how people, how people, most of the normal people even live their lives. Because it is now more cost effective to use a robot in logistics than it is to use a human. And that's really good news for the environment. It's really good news for neighborhoods that are going to get quieter and safer and less traffic, less pollution. And it's really good news for customers, because you can get things way faster and more reliably and for less expensive. You know, our customers love like, there are obviously so many cool things about the system that, you know, you can talk about and that you see customers taking advantage of. But like, no tip, exclamation point, exclamation point is like a big, you know, that's probably the number one comment. I think that customers love not having to feel guilty and being able to just have a system that they know how much it's going to cost. Well, thank you, Keller and Eric for being here with us. I thought you were going to say it takes longer than you thought, not 10x more than it costs. But anyway, that's a great, great way to end. It does also take a lot longer. I mean, I think, you know, the memo that Sean wrote here at Sequoia a few years ago, I think is like, is deeply true. I don't know if you'll ever publish that publicly or if it'll be allowed. But I do think, you know, suffices say there's an internal Sequoia memo that has had a big impact on me talking about A, why hardware companies are going to be some of the most impactful companies for humanities progress over the coming decades. And B, why it's super hard to get those companies off the ground and fundraise for them and see like how, you know, investors should think about funding those kinds of companies. It's interesting like when you look at the world today to see, I mean, wow, how fast the world changes. Because think that we spent 10 years being the freaking black sheep, like a hardware company. No, thank you. Like, like, let's invest in, you know, SaaS, let's invest in margins. Like this is where the whole future was. And like, you know, iPhone apps, blah, blah, blah. So I don't know. I guess I feel like, you know, pain, remember, pain and Batman, what does he say? Like, you adopted the darkness. I was born in it. Like, and we built a robotics company for 10 years before building a robotics company. It was a cool thing to do. But, you know, I do think that especially important for like US competitiveness and just for our ability to like, build the future that we'd be really proud to hand to our kids and to our grandkids and to build the sci-fi version of the future that we were all promised. Like, we got to get good at building stuff again. And we got to get good at building not just, you know, vehicles and hardware. We got to get good at building infrastructure. Like, we're depending on the crumbling infrastructure that our grandparents built for us. I read the other day, the, you know, we just installed these like anti-suicide nets on the golden gate bridge, if you guys are about that project, it cost more to install those nets than it costs our grandparents to build that bridge. I believe it. So anyway, we get really excited just like, we think the future, like promising future is like, we should be able to build infrastructure. You know, we got to, we have to be interested in it. And we, I think people have to have this stomach for it. And we have to learn how to manufacture and run complex supply chains again. And we have to be, you know, bold and like believe in sci-fi versions of the future if we're going to build them. Awesome. Let's end it at that. Believe in the sci-fi future. Yeah. Thank you guys for being with us. Thank you. Thanks very much. Thank you for inviting us. [Music]