Title: The Thesis: Why Uber Will Win the Autonomous Vehicle War Show: Joseph Carlson — Qualtrim Studio (Deep Dives) Guest: Joseph Carlson (solo) Date: 2026-08-15 URL: https://www.qualtrim.com/app/studio/watch/0edef393-0e69-4c9e-a7f3-8114c99123a3 Length: 38:58 Note: Transcript from the in-page English subtitle (WebVTT) track; (mm:ss) cues real, grouped ~25s; login-gated self-hosted video (no youtu.be deep-links). Auto-caption spellings ("Avs" = AVs, "leader" = lidar, "writers" = riders, "neuro" = Nuro, "lift" = Lyft) left as captured; wording otherwise verbatim.
(00:00) Welcome everyone. It's time for another installment of the thesis. This is where we dive in deep on companies, and I take a different perspective here. The goal is not to just learn about a company and read the pamphlet or the Wikipedia overview of it. That's not what we do here on the thesis. The goal is to change your perspective on it, to give you new insights that you don't find anywhere else. Hopefully, by the end of this video, you've gained something in the way that you look at these stocks, because in many cases, being able
(00:27) to look at the same information but understand it differently is how you have alpha in the market. And in this case we're going to be looking at Uber. This is a stock that I believe has an enormous and ongoing aggressive bear and bull case argument. Bears argue that Uber is a ticking time bomb. It's a stock that will continue to go down because of Avs.
(00:52) That is the big the big risk with this company. And we're going to look at that bear case and try to examine it through a very in-depth lens. Well, also be looking at what I believe is the bull case for the company and how to understand the AV risk, how to put it in context, and why I believe investors are missing the proper lens of viewing this. Now, like I said, as I'll be addressing what I believe is the primary debate over whether Uber will be a successful company and very fortunate
(01:17) and prosperous over the next ten years, or whether it will get disrupted. We're looking at AV companies and specifically in this case, Waymo. Now they're not going to be the only one. There's going to be other AV companies as well. But we can start off with Waymo because they're the one that has proven themselves so far. And to look at the threat and the potential growing threat of Waymo, I think it's important to look at how Uber scaled so fast. How did they grow? How did Uber start and grow so rapidly?
(01:43) It was actually baffling. Look at the looking at the growth of Uber. The rate at which they scaled from city to city is incredible. They were opening everywhere, all over the place, all over the planet at the same time. They were opening in Miami and in Texas and California and New York and Paris and United Kingdom everywhere. They're all over Europe, they're all over Japan. They're everywhere that you can be all at the same time.
(02:09) They scaled super fast. They got to scale faster than most companies do. And how did they do that? Well, if you look at it from Uber's perspective, the way that they describe it is they are a supply led business. Meaning when they move into a new territory, you look at all the things that you really need to run Uber's business, all the investments needed. You have roads. Those are something that you need.
(02:34) So for Uber to work, you have to have at least some roads. Well, across most of the Western world we have big asphalt roads, we have city streetlights. We have an entire infrastructure made that taxpayers have paid for. So that's covered. Uber doesn't have to worry about that. You need gas stations. You need fueling centers. That's also covered. The city doesn't pay for that, but gas companies do. Chevron does. So that's also covered for Uber as well.
(02:59) But Uber also needs drivers. Let's cover it as well. Every city that they go into, there's people that that have jobs that they have time to to do driving if they have some free time. In fact, a lot of people, if you just have an app that they can install and you say, hey, can you accept drives for the next three hours and earn some side money, they will click. Yes. Very easy. So Uber has the roads. They have the fueling stations. They have the drivers. Oh man. But they need those cars. And those cars are expensive.
(03:26) How could you have millions, tens of millions of cars? That's going to take an eternity to build. Think about all the CapEx they'll need. They already have cars. Every territory that they move into, the drivers have cars sitting in their garage. As you know what, most people use their cars for maybe like an hour or two a day. So for 22 out of 24 hours a day, those cars are sitting, going unused 95% of a car's life. It's sitting in a garage or sitting in a parking lot.
(03:53) So Uber saying, hey, why not? For just a couple more hours, you get some use out of that car and make some money with it. Uber moves into a territory where everything already exists. They're not building anything. They're not investing anything into that territory. They're not buying parking lots. They're not buying land. They're not hiring employees. They're not buying fueling stations. This allowed them to rapidly scale everywhere across the globe.
(04:22) But what Uber did bring was actually very valuable. And I don't mean this is a way of discounting their efforts because you might say, oh, well, Uber is not really doing anything. That's not true. Uber is actually a very complex and a very difficult problem to solve. What Uber did bring to the table is asset utilization. So think about this. You have all these people. You have cars. You have gas stations. You have people willing to drive that have free time. You have roads and infrastructure and you have
(04:49) and then you have people needing rides, right? So everything already exists in these territories, everywhere that they went. All the ingredients were there. They're just needed to be someone smart enough to take all these ingredients and work them together in a special recipe that makes all of it work. Well, that's what the Uber application did. That's what the brains of the company did. Uber specializes in making technology that deals with dispatching,
(05:15) that deals with peak demand, that deals with onboarding processes, logistics, dealing with all of that stuff, all of the incentives or drivers and the flex pricing and the demand and all the infrastructure behind making all of this work together should not be discounted. It is very complex. There's a lot of smart people working on it. Uber has specialized with this technology for a long period of time, so I don't mean to undercut the logistics efforts of what Uber has created,
(05:40) but I do want to express that the part of the big reason that Uber was able to scale and grow so fast was that every single thing needed for them to be successful already existed before they even moved in. All they did was they used all the things that were there. They applied this big algorithm to it that made it so that it was being allocated correctly, so that the time and energy and money of all these different assets were being allocated correctly.
(06:07) In fact, people view Uber as like a ride sharing business, but I view it more of a physical asset allocator. They're taking all of these different assets writers, drivers, cars, all of this physical infrastructure, and they're making it so that all of it communicates all of the logistics work together. All of the assets are being allocated in the right way, and everything's working smoothly. That is a whole different look at the company
(06:32) and what they actually specialize in. So a lot of investors are still viewing Uber as a ride sharing company, which they'll refer to it as a ride sharing company. And that's a nice euphemism for what they're doing. But if you actually look under the hood of what the company is actually doing, they are basically a physical capital allocator. They're taking things that already exist in an area. All these physical things, whether it be drivers and riders and cars and infrastructure and fueling stations, all of this already existed,
(06:59) and there was already demand for ride sharing that already existed prior to Uber's arrival. But they didn't know how to communicate. Drivers didn't know how to say, hey, I'm free to give a ride right now, and people that needed a ride didn't know how to say, hey, can someone pick me up? That's qualified driver to give me a ride? There is no system in place to make all of these assets communicate with each other in a very efficient and fruitful way.
(07:25) So that's really the problem that Uber solved. They moved into a city. They used all of the different assets that already existed prior to their arrival, and then they helped them communicate with each other in an efficient way. Now that communication and the problem that they solved, again, can't be understated, making it so that the whole logistics, the whole surge demand the reputational system of the drivers and the riders. You know, so that they know if you're a good writer, if you're a good driver,
(07:50) you can actually accept a ride from this person. All of this stuff took time to figure out. So Uber put a lot of effort into that communication system, into allocating all of these resources properly. That's the whole that's the whole thing that Uber offers. But it's not just a ride sharing company. What they've done is they've made everything work together, that there was already demand for in a way that it wasn't working together before. And that is a big problem to solve now,
(08:17) since everything already existed that they needed to make this work. It allowed Uber to scale like crazy because, again, they weren't limited by any CapEx investment. They didn't have to build any part of the infrastructure along the way that a centralized system that dealt with all of this asset allocation, making all of this communication flow together really well. They had all the logistics know how, so they could replicate what they were doing from one city to the next.
(08:42) It didn't really matter whether or not they're in LA or whether they were in France. Did it matter? They could use the same type of logic in both areas. Now, building this up was also again supply led. So they would come into an area and they'd first work on getting the drivers before getting the riders. They'd just get the drivers. They'd get people out in the city ready to accept rides. And when you don't have demand yet, you don't have the density of people knowing about your app.
(09:09) In some cases, Uber had to subsidize those drivers. So like if they go into a new city, they don't immediately have millions of people using their app every day because it's a brand new city. People haven't had a need to download the app. They haven't used it before. So Uber needs to first create the drivers so they get hundreds of drivers. They get them going around a city. They say, hey, start, start taking rides. But there's not enough demand yet because people don't know about it.
(09:34) So Uber has to literally pay those people to sit out there and do nothing, just waiting for rides. Just if one pops up. So Uber subsidizes it a bit at the start and that's where their cash flow went. Their cash flow went from saying, hey, let's pay for riders to get out there and or sorry for drivers to get out there, and then we'll start to get the riders. But as soon as riders are aware that there's going to be someone to pick them up,
(10:00) if they use the Uber app, they quickly start downloading the Uber app and use it, and then they can get more, more drivers. Then they get more riders, then they get more drivers, then they get more riders. But there is a little bit of subsidizing and paying for those drivers to sit around to begin with. That was basically the only investment they had to do. They didn't really have to build anything. They just had to get some people on the road before they could accept riders. Now, the big thing that they work towards in every area that they're in,
(10:27) the most important thing in this entire industry is density. That is the word. It's not scale because scale means you're going everywhere across the globe. No. It's density. Density is the most important word. Density means that in a single area that you're operating in, you have enough critical level of riders and drivers to make the system efficient. So, for example, if I start Joe's ride sharing service
(10:53) right where I go around and I offer rides, that would be a great idea. Maybe I can copy Uber and I can do that. So I start my ride sharing business and I start my own app and I say, hey, I'm available to give someone a ride, and I'm here. And then someone says, yes, I'll take a ride. And they're on the other side of the city. Well, I'm the only driver, so I'm going to have to drive to the other side of the city just to pick them up. That's no fun. I'm going to spend an hour and a half or an hour just driving to get to them.
(11:20) I'll burn a lot of gas, a lot of my time just to get to them, and they have to wait for an hour before I even arrive. That's not going to work at all. It would be nice if there's another driver on that end of the city that could accept that, right? That's the whole issue that density solves. When you have critical density, it means that you have enough drivers that literally anywhere there is a rider, there is a driver close by making it
(11:46) so that the wait time for your driver to arrive is near instantaneous within 15 minutes. Right, right. When you get the app and request one, there's one right around the corner showing up. That's density. That's what makes the product work because it has a magical flywheel. Not only does the writer have a much better experience because they go, wow, anytime I go to Uber and I click anywhere I am, I need a ride. I got an Uber driver right around the corner
(12:13) picking me up like within ten minutes. That's great. It's just dependable. I don't even need to plan ahead. I can just do this wherever I want. Boom. I have a driver picking me up, so the passenger in that case has a wonderful experience. They want to use Uber. Getting someone very quickly to where they are is important. In many cases, people use these services too because they're traveling. They have flights to catch. They have hotels to check into.
(12:38) They have events and conventions to go to and ballgames to get to. So it's time sensitive. So density is everything. It's the name of the game. You need to have density because the passenger, once they ride quickly, they don't want to wait for someone on the other side of town to spend an hour getting to them. That's density, but it's also density on the driver's side. See, a driver doesn't want to say, yeah, I'll accept rides for the next three hours and they go out, and then they just kind of drive around
(13:04) waiting for waiting for a passenger, waiting for anybody to want to ride. That's not what a driver wants to do. A driver wants people all the time saying, I need a ride here. I need a ride here. And there are only five minutes away. See, from the driver's perspective, the higher density makes it so that they also have a better job. They're getting paid more. They're doing rides. They have passengers in the car the majority of the time that they're out, instead of just driving around hoping that a passenger will show up.
(13:31) Density solves both sides of the equation. It makes the experience much better for the passenger, which makes it much more repeat business. They choose Uber over other services because they get rides so quickly after requesting them. It also makes it much better for the drivers because when they go out, they have a higher utilization, meaning they're being used more throughout the day. The passengers are closer to them, they have to travel less time to get to the passenger, and it makes they have a higher earnings
(13:59) because they have more time with passengers in their vehicle, which makes them earn more. So density is everything that is. That's the whole goal to get to. And luckily for Uber, because they had all the existing infrastructure in terms of all the assets there, you have the not only the driver and the riders there, but you have the cars, you have the roads, you have everything you need to build the service. When they are able to scale up, they got to critical density
(14:25) faster than anyone else. And because I start Joe's writing service, where where I offer rides and people are on the other side of town asking for a ride that's not going to work. So competitors like lift their out of it. They don't have a chance, they don't have density, and nobody's going to use them because they already don't have density. Nobody wants to wait 20 more minutes per ride, even if it's a little cheaper or whatever incentives they get. People want rides quickly.
(14:51) They're going to go with the service that has the critical density. Now, critical density is a term I also use to describe, like the the amount of density that makes it so that you're at peak efficiency, so that anybody that requests a ride gets one in a timely manner, and any driver that goes out can, can get a rider. Right. So that's critical density. If you fall below critical density, that's where your metrics actually suffer.
(15:18) That's where Uber would actually be in trouble because now they're they're having to go back to like paying drivers to ride around and trying to give incentives for riders to use their services. And what's happened is Uber's basically been able to get to that critical density level, the really profitable peak density, where everybody's happy in the majority of territories that they operate in. That's the incredible thing about it. They are growing to that critical density in so many areas.
(15:44) So, well, right now they've already achieved it in so many areas, and that's what's made them scale to a very profitable company. So that's the important path that Uber took. Now let's talk about the risk of Avs. In contrast to Uber. The path that autonomous vehicle companies have to follow to scale is very, very different. And this is where we compare and contrast to Waymo. As you can imagine, it's very different than just going into a territory
(16:11) and becoming this asset utilizar this, this company that comes in and says, hey, we can use these people and these cars and these roads and these charging stations and these gas stations make it all work together. That's what Uber specialty was a Thomas vehicle. Companies like Waymo, they come in and they say we need to build 1000 Avs 1000 cars. This is a massive CapEx, you know, $100,000 with all the sensors.
(16:38) The leader, we need to make all the technology that tests the vision of these Avs so they can drive safely. We need to map and light our all the roads. We need to map them all out. Like like you're going and making an actual three dimensional map of the entire city. We need to test those tricky areas. So the project of just entering a city is immensely more difficult and challenging for Waymo. Not only that, it's far more capital intensive.
(17:03) They need to buy the cars they need to lease out, or by the parking garages and parking spots for all those cars when they're getting maintenance, or when they're going back to charge all the things that they have to do. So you'll see big Waymo parking garages with like hundreds of Waymo's in them. And then you have the constant maintenance challenge of these vehicles where you're constantly dealing with things that break their expensive vehicles. They have leader and all this technology built in them.
(17:29) Now, the company doing this, of course, Waymo is very smart. They have Google's backing. So they have a lot of capital to be able to invest in this. But even capital, even with that, it's still just way more difficult. Like just building the vehicles is a physical challenge. So at the very onset, it's going to be a much slower path for Waymo to scale than it was for Uber, that we just know that's the case. It's just not going to happen as fast because you're scaling a digital business compared to a physical business.
(17:57) A physical business is just going to take longer. And then you have some other things that are different dynamics. So in terms of the growth path, Waymo is going to take much longer. And then there's also some things that I think are misleading some investors today. So for example a lot of investors look at the success of Waymo in California. And they're using that to extrapolate the success that will have everywhere across the world.
(18:22) But what Uber has tried to outline is that San Francisco is a uniquely favorable territory for autonomous vehicles, uniquely favorable. For example, look at this chart here. It shows that San Francisco's population density, which is the zoom in on it. Here it is the black bar here the dark blue bar. It's like twice as dense population as Los Angeles.
(18:48) And it's about 4 or 5 times as dense as the United States top 50 cities on average. Now why is that important? Because a dense population actually favors autonomous vehicles more than a spread out one. So when you're looking at San Francisco and you're looking at Los Angeles, these are dramatically more dense in population than the average US city, meaning that they started in the places that are by far
(19:15) the most favorable to Waymo's. So that's one thing. Another thing is that Waymo is actually a bit more expensive than Uber. The pricing of it, just because you're getting a ride without a driver, it's kind of a private experience. It's considered like a better experience. And San Francisco is actually about twice as affluent as the average city in the United States. So you look at this and it has the bar here again, far more fluent people that can afford to pay that premium.
(19:40) And they also point out that San Francisco is unique in that it has shorter trip distances. It's about half the distance of the average US city, which again, uniquely favors autonomous vehicles. And the reason that Uber points this out is just to point out that there's a reason that Waymo started with San Francisco, because it has unique attributes and unique demographics that uniquely favor autonomous vehicles. So they pick the place that there would be the most successful, and it would be a mistake for investors
(20:06) to look at San Francisco and the success that Waymo's had in that city, and to extrapolate that everywhere else, because San Francisco is unlike most U.S. cities, most of them are much more challenging for a company like Waymo to be successful. Now, that doesn't mean that they won't be successful anywhere else. It just means that they had a unique advantage in San Francisco, and they're likely not going to have as high as utilizing utilization, as much profitability, as much uptime for their vehicles and pricing in these other cities.
(20:34) Now, the other thing that I think is challenging for AV companies like Waymo, not just the technology behind it, not just the CapEx, but there is another challenge, which is that the amount of people that need a rides it varies greatly. It has big surges of demand, meaning it's not a thing where from six in the morning till 9 p.m., the amount of people asking for rides is the same and constant every hour.
(21:01) It's a little bit like restaurants. You have the dinner rush, you have a lot of people wanting to eat food at the same time. Well, the same thing with the ride sharing business. You have a lot of people wanting rides at the exact same time. Well, think about this challenge from the AV company perspective. You have like 100 vehicles in a city, so during 7 p.m. you might need 300 vehicles to get all these writers at the same time. Okay, so then you pay for 300 vehicles.
(21:27) Well, any other hour besides 7 to 8, you only need 100 vehicles. So what do you do with the extra 200? This is the problem. Either you over invest to go to peak density hours. To go to the peak hours you over invest where you can. You can handle peak hours, but then you have too much too many assets on the road during all the other hours. And you have wasted a lot of money because most of the time you're not going to be using all of those assets, all those vehicles, all you you invest
(21:56) just enough to get the normalized hours like the the rest of the day. But then you can't service everybody on the peak hours. See the problem that Waymo has, they either over invest for those peak hours or they under invest and they get just the normalized hours without the peak. This chart illustrates this dynamic and this is really important. Uber's network delivers the highest AV utilization against highly variable demand.
(22:21) So variable demand is such a problem for AV companies. This highlights it right here okay. We have one this dark blue category stable engagement for Avs which serve as a fixed base load supply. So an AV company it can scale up in the morning a little bit. And then they ran out of vehicles. They're out of vehicles right there. So they can only go to about half their normal demand.
(22:46) But then look at this. The actual demand. Two human drivers, the light blue here can scale up twice as far and it can scale down and it can scale up even further three times as far. Human drivers provide boost supply to meet demand spikes throughout the week. So the way that this works is, again, I could be an Uber driver and I'm sitting at home right, not doing much. And then the hour it gets to this hour
(23:12) right here where the it gets right here where it's starting to peak demand. And Uber knows that there's going to be peak demand this hour. They have predictive technology that says, we know in the next hour we're going to have a lot a lot of writers asking for rides. So what they do is Uber puts out the bat signal. The bat signal is setting out toast notifications to like a thousand drivers, hey, would you like to do rides for the next two hours? It's going to be a lot of demand.
(23:37) So you go, yeah, I'll do it. Boom! You get out there, you get in your vehicle, and then Uber says, hey, go to this location. There's going to be people in ten minutes asking for rides, right. Predictive predictive technology. And then all the Uber drivers get in their vehicles, they go out and they can fulfill this peak demand. That, again, is highly dynamic. How do you do that with Avs? How do you deal with this peak surge in demand, this highly variable demand
(24:02) every single day? Either you have like a million cars on the road which aren't being used the majority of the day. They're just massively depreciating assets that need licensing. And they need, you know, they need all this stuff that that the company would have to pay for. So you either way over invest or you invest a reasonable amount, but then you can never truly satisfy the peak demand. That's such a difficult equation for them.
(24:28) It says number three, an avalanche competitor must hold significant, underutilized or underutilized supply to match Uber's reliability and price, or deliver worse consumer experience and leave significant demand on the table. So you have to pick one of the two. This is an ultimatum, and that's not a false dichotomy. Like they literally have to pick one of these two. They must hold significantly underutilized their supply.
(24:55) So they must have like 1000 vehicles that are underutilized 90% of the day, or they deliver the worst consumer experience and leave significant demand on the table. They got to do one of those. And so far I believe the AV companies are picking the latter. They're basically just saying we're going to get most of this stuff, like during the stable part of the day, but we won't do the peak hours. They have nothing to do. The highly variable demand when you get to
(25:21) Friday, look how much this spikes up on Friday. It literally spikes up like six times. What an avid can handle. How many vehicles are these AV companies going to going to put on the grid? The only one that that could possibly theoretically do this is if Tesla gets approved everywhere. But so far Tesla's not using leader. We only see it in small little areas, and the whole dream of it seems somewhat fanciful. So far.
(25:46) We only see Waymo as the real threat doing this, and they don't have the vehicles to match this type of peak demand. So when I look at this, this is a massive problem for AV companies. It's not just the problem of them having an expensive and very difficult ordeal and matching Uber's scaling, but it's also a problem with their peak demand flexibility. And the more that you learn about this dynamic, the more difficult it is also during like huge events, for example,
(26:14) like the World Cup events and major sporting events, AV companies like Waymo are trying to handle demand where they they bring over like Avs from different cities to go to that event, and they try to put them in their network. They try to do stuff like that. But it's really difficult. It is very difficult for them to get it right. Uber has a technology to be able to easily handle all of those type of events, any type of event that has lots of flights,
(26:39) lots of massive spikes in demand, they can handle that. Now, what I want to point out to, and I think that this is the biggest point, the one that investors are missing the most is right now we have Waymo going city by city by city, and they're just opening up in all different cities at the same time. And they're testing in all different cities. And from the headlines, that looks really bad as an Uber investor.
(27:05) And I was on the same as in the same case, I was scared about that. I thought, man, that doesn't look good. But when I learned about this more, I realized that Uber can be very late to the game on this and still be in the lead. So, for example, I think that Uber could literally take another five years to get any type of autonomous vehicle in their grid working and they would still be number one above Waymo,
(27:30) even if Waymo has gone into like ten more cities at that point. And the reason why is simple. It all comes back to building density. That is the problem for Waymo. And it's not the problem for Uber. Uber's big challenge is to to keep critical density. Now if we look at the difference between critical density and peak density right here, this dark blue part is probably critical density.
(27:55) This is where you're getting just like the normal rides. And then peak density right here is is the very top part. Right. So Uber needs to keep enough density that their service works really well, which is that critical density. Waymo will move into these cities. But even in the cities that Waymo's very mature in, they have not they've not knocked Uber out of critical density. Uber still operates incredibly efficient in every city, even the ones that Waymo's directly competing against them with.
(28:23) So nowhere has Waymo knocked them out of critical density. And that is the important thing because again, Uber can get they can get to the game. Five years later, we can be in 2032. And then finally Uber starts to get autonomous vehicles in their network. And the the differences is all of these years while while Waymo has been working towards density, Uber can skip front to the line. So I view it like this.
(28:49) If you want to put some imagery behind this, think about waiting in a line for Disneyland. Okay, you're waiting like for the Star Wars line, and it's like an hour and a half long and you're just waiting and waiting, zigzagging through all the lines, going through everything. You know, you're kind of leaning against the fence and it feels like a long time, right? Those lines can feel really long. And then you get to the very front of the line, and someone else
(29:16) pays the $50 or whatever to skip the line and go and right in front of you. That's basically what Uber is doing. If you look at what they can do, since they already have a density, critical density in every area that they operate in, they don't have to rebuild density from the ground up from scratch. As an autonomous vehicle company, they can take any amount of Avs. They can plug them into their existing network, and instantly
(29:41) those Avs will be 100% utilized all the time. That is completely different than the challenge of Waymo. Here's a graphic that I believe illustrates this concept. So when Waymo enters a new market, they have you know there's there's these writers and then they have the cars and they have to build up this relationship. They have to get the flywheel going right. So they have to make it so that they have all these vehicles everywhere all the time.
(30:08) And enough that when a rider ask for a ride, remember the rider wants to get it within 15 20 minutes. They don't want to wait an hour for it. So getting that density is huge. You must build density first. Low initial efficiency, lower utilization until enough cars and demand are concentrated. So here's another visual to illustrate this. We have early deployment of Waymo when what they're doing is they're going into an area.
(30:34) They have their initial, you know, getting ready. They have the leader mapping, they have the permitting, they have the testing and everything they need to do. They have to getting, you know, building all the vehicles, getting the parking lots, getting everything in order. There's a lot of preparation work that goes into just launching in a new city. But even when they're operational, they have a sparse fleet, limited availability across the city. Gaps in coverage equate to longer wait time.
(31:00) They're building more vehicles and getting more vehicles deployed. That builds momentum over time, but at first, the first couple of years, they have longer wait times. They don't have that critical density that they need. Then, years later, Waymo will eventually reach their critical density. They'll get faster pickup times, citywide coverage, stronger network effects. Okay, so Waymo already gets there. When they get to this point. There's shorter waits.
(31:25) Most rides arrive, you know, in a matter of orderly time, reliability and better service. So Waymo has its flywheel. It's getting to this point from early deployment into peak density. Now this takes a number of years because again, of how difficult it is to scale such an asset heavy business when there was so much red tape. We compare this to what Uber is doing in their scaling of Avs.
(31:51) So Uber today already has critical and peak density. And everywhere that they're operating they have enough drivers. They have the whole network embedded. Right. So they already have this scaling part done. They've already got the density. This means that supply already is built out across the city. Wait times are already short. They already have high utilization there, strong existing demand, faster pickup times and dense driver networks.
(32:17) So the challenge is now what do they do with Avs? Well, Uber is working with a lot of partners to develop Avs in-house and to license them. They have lots of different people doing different approaches, and Avs are impressive technology. But it's no ASML machine. It's not something that no other company can ever feasibly do. We know how it works. They use leader and they use cameras. And they use a system that that they test that allows cars to drive by themselves.
(32:46) Again, this is not an insurmountable challenge and I have a degree of confidence to other companies will figure this out over time. There's already ones that are well on their way with the autonomous driving technology. The more challenging part, I believe, is building a network that has the level of density with autonomous vehicles. Luckily for Uber, because they already have the densest network, they can literally take any amount of Avs, whether it be ten or 100 or 10,000.
(33:14) They can plug them into an existing network, and those Avs will have 100% utilization right from the start, because they're already going into an existing network. There's already the demand density at play. And then the Avs, for example, if they use neuro those Avs, they could order like 100 Avs and put it in a city and they just go and accept rides, and then they would have the humans fill in the highly variable demand.
(33:40) So you have instant dispatching. Avs are immediately matched into existing flows of rider demand. Immediate utilization. Autonomous vehicles can reach 100% utilization right away. You don't need to have the same level of build out, no multiyear build out for density. You know, everything is already existing. You're plugging this into something that's already existing. And I think that's the big thing that people need to understand
(34:07) is that Uber can wait years and years and years to get to the point where they have Avs. Then once they have that, they're plugging it into an existing network, which makes the challenge much easier than what Waymo is trying to accomplish, or any other new AV companies trying to accomplish. So Uber can be late and still be first. That's the point here. They can be five years late to Avs and still be ahead of Waymo.
(34:32) I know that's confusing, but it's just the fact that they don't need to build up that density in every area. They've already done that. The other thing that these graphics leave out, that I think is another big part of what I believe investors are missing with Uber is that if you have this situation, let's say that in five years that Uber gets to a place like 2031 where they have a mixture of other Avs mixed into their already existing networks.
(34:58) So they have hybrid autonomous vehicles plus human drivers. That is a superior situation to this one. Just autonomous vehicles. This the the hybrid between humans and Avs is superior to 100% Dave's. So even when you get to the finish line, Uber has a better product. Why is it a better product to have the hybrid?
(35:23) Well, we go back to the variable spikes in demand with the 100% autonomous vehicle network. How do you deal with these spikes in demand? You either have under utilization of a massive amount of vehicles, so you build enough vehicles that you can get these peaks and you under utilize them the rest of the day. So 90% of the time they're being underutilized, or you leave demand on the table for other companies to take like Uber,
(35:49) the only way that you can deal with this highly variable amount of demand is with humans. Because humans, you're not paying for their vehicles. They can park their vehicles in the garage and go about their day when they're not being utilized. So even at the finish line, if they end up both getting to the same place, advantage still goes to Uber. There's only one situation where I believe that Waymo actually wins
(36:14) and actually disrupts Uber, and it is if it takes an insanely long time for Uber to ever get any Avs into their network, I think if it takes a decade, like ten years, then Waymo may have. So they've built they've had so much time that even as slow as they are scaling, I think that's enough time that they could take meaningful and I'd say critical market share in a number of big cities. That's the bare case for Uber.
(36:40) So Uber is on a time crunch. They need to and I would say 5 to 7 years start to integrate Avs into their network. But I don't think the time crunch is nearly as severe as being priced into the stock. They have a long time to be able to figure this out. They already have a very good product, even in the cities that Waymo is very mature in, Uber still utilized and doing incredibly well. So I hope that this illustrates the unseen bull case for Uber.
(37:07) It is the fact that they were unique in the way that they could scale. AV companies like Waymo can't scale in the same way or the same speed. It is the fact that AV companies can't deal with the flex demand in the way that Uber can, and it is the fact that even at the end of the day, when they both have their end product, a hybrid model between human and Avs or 100% Avs, all of those advantages go to Uber. When I look at this
(37:32) all in concert with each other, I believe it's highly likely that Uber continues with a great deal of the aggregation demand. Typically when supply is commoditized, when there's tons of different Avs and different companies that the aggregator usually wins. And that's what happens in all different industries. Like Netflix is an aggregator, and YouTubes and aggregator booking holdings is an aggregator, the App Store from Apple and the Google Play Store.
(37:59) Those are aggregators when when other things are commoditized and there's lots of supply, the aggregators usually win. And in this case, I actually think that Uber has an enormous amount of advantages. So as you're looking through your Uber, you know, you're looking at the stock. You're going to see constant headlines of the advancement of Waymo over the next five years. That's going to happen.
(38:24) And it might keep Uber stock down a little bit because investors get gloomy whatever that happens. But just know the timeline. Know that Uber can be very late to the game. They can afford to be very late to the game, and they're still first and know that when they do end up getting the final product, which is a hybrid model of Avs and human drivers, that is a better model. It's a better product than any 100% AV network. Both of those make me feel much more confident
(38:49) in this company being ultimately successful than what I believe the market is currently pricing in. So that is this thesis episode. Hope you enjoyed. We'll have more of it soon.