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It was difficult in the beginning to answer the question I got from everyone: what’s it like living in America again? After all, I had been coming back to Wisconsin in the summer for years, and my move back happened in the summer; things mostly felt like more of the same. Then, the leaves started turning colors, the air became chillier, and, as daylight grew shorter I came to relish one decision in particular: living in the suburbs.
There is much to be said for urban life, and I was certainly spoiled in that regard living in Taipei. It always seemed odd to answer the question “What is the best part about living in Taiwan?” with the word “Convenient”, but that’s the honest truth: everything you needed was within walking distance, the subway was extensive, clean, and reliable, and, once you understood that traffic was governed by the rule of rivers (the bigger you are the more right of way you have), driving really wasn’t that bad either.
When my parents first moved away from Wisconsin I needed a new place to stay for the summers and, concerned about upkeep of an empty house in the harsh winter, I opted for a downtown condo; it helped that downtown Madison was a beehive of activity with the university and state government, and I liked the idea of walking everywhere. Then came COVID and the summer of 2020, and suddenly downtown wasn’t so busy anymore; I found myself driving more than I expected, and feeling rather sick of condos, which I had lived in my entire adult life. And so, when a house opened up near an old friend, I snapped it up, remodeled it to my liking and then, this past year, decided to live there full time.
It’s fashionable to hate on the suburbs, particularly for millenials just a bit younger than I am; I was born at the tail-end of Generation X, and my experience in small town Wisconsin was one of leaving the house in the morning on my bike and not coming home until dinner, hopefully in one piece. It was, all things considered, pretty idyllic, but I can imagine that the clampdown on youth freedom that happened over the last few decades, along with the rise of indoor activities like video games and smartphones, made the suburbs feel increasingly alienating and isolating. What a relief to move to the big city, particularly in the 2010’s when Uber came along.
Uber Resolution
There were, in the 2010s, few companies more contentious than Uber, and not just because of the scandals and willingness to operate in the gray area of the law. There was a massive debate over whether or not the company was even a viable business. Hubert Horan, in his seemingly never-ending series insisting that Uber would never be profitable, twice attacked me personally (and dishonestly) for believing that Uber would scale into profitability:
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One does not have to immediately accept all of those conclusions to see that Thompson’s various claims suggesting that Uber might someday have a viable welfare enhancing business are not backed by any hard evidence about efficiency advantages or sustainable profitability. All of Uber’s growth has required massive investor subsidies — $2 billion in 2015 and $3 billion in 2016. All of these subsidies have been destroying competitors who are more efficient but can’t withstand years of subsidies from Silicon Valley billionaires. Thompson argues that Uber has grown total market demand and offered greater service options at night. True, but all due to unsustainable predatory subsidies. Thompson says that Uber’s app gives it the great competitive advantage of controlling its customers. False — people don’t like Uber because the app has a neat user interface, people like Uber because the app shows more cabs at lower prices than competitors can offer. All of those cabs and low prices are due to unsustainable, predatory subsidies. Thompson insists “the fact remains that both Uber riders and drivers continue to vote with their feet” justifies his belief that Uber’s approach to regulation is right, but again ignores that they are not voting for the more efficient producer, but for massive service subsidies. Thompson is falsely claiming that Uber’s growth reflects the free choice of consumers in a competitive market. Uber’s predatory subsidies are designed to undermine the processes by which competitive markets help allocate resources, and then to eliminate competition altogether.
If these benefits were created by legitimate efficiencies, as Thompson imagines, there would be evidence showing how they made Uber more cost competitive, or how they similarly transformed competition in other markets. To refute the points here about Uber’s predatory, market-distorting subsidies, Thompson would need evidence that Uber has scale economies powerful enough to quickly convert $3 billion operating losses into sustainable profits, and evidence that Uber has competitive advantages overwhelming enough to explain driving everyone else out of the industry. Since Thompson does not have any of this evidence, he can’t claim Uber has produced benefits for anyone but itself.
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Well, here we are in 2025, and over the last 12 months Uber has made $4.5 billion in operating profit, and that number is trending upwards (and doesn’t include the significant profits Uber makes from its non-controlling interests in other mobility companies that it gained thanks to its aggressive expansion); no, I didn’t have evidence of that profit in 2017, but I did understand how scale works to transform money-losing software-based Aggregators into profitable behemoths in the long-run.
Another classic of the Uber bear genre was this 2014 post by NYU finance professor Aswath Damodaran attempting to determine Uber’s true value; the startup had just raised $1.2 billion at a $17 billion valuation, and according to Damodaran’s calculations, “it is difficult to justify a price greater than $10 billion” (his actual valuation was $5.9 billion). Investor Bill Gurley — before his dramatic powerplay that led to the ouster of founder Travis Kalanick — explained what Damodaran got wrong in How to Miss By a Mile: An Alternative Look at Uber’s Potential Market Size:
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The funny thing about “hard numbers” is that they can give a false sense of security. Young math students are warned about the critical difference between precision and accuracy. Financial models, especially valuation models, are interesting in that they can be particularly precise. A discounted cash flow model can lead to a result with two numbers right of the decimal for price-per-share. But what is the true accuracy of most of these financial models? While it may seem like a tough question to answer, I would argue that most practitioners of valuation analysis would state “not very high.” It is simply not an accurate science (the way physics is), and seemingly innocuous assumptions can have a major impact on the output. As a result, most models are used as a rough guide to see if you are “in the ball park,” or to see if a particular stock is either wildly under-valued or over-valued…
Damodaran uses two primary assumptions that drive the core of his analysis. The first is TAM, and the second is Uber’s market share within that market. For the market size, he states, “For my base case valuation, I’m going to assume that the primary market Uber is targeting is the global taxi and car-service market.” He then goes on to calculate a global estimate for the historical taxi and limousine market. The number he uses for this TAM estimate is $100 billion. He then guesses at a market share limit for Uber – basically a maximum in terms of market share the company could potentially achieve. For this he settles on 10%. The rest of his model is rather straightforward and typical. In my view, there is a critical error in both of these two core assumptions.
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Gurley argued — correctly in retrospect, given that Uber’s gross bookings over the last 12 months were $93 billion in rides and $86 billion in deliveries — that Damodaran failed to consider how a radically better experience could dramatically expand the addressable market, and completely missed the potential for network effects leading to an outsized share of that expanded market.
I do feel Uber’s effects even out here in the suburbs: when I lived in Madison decades ago, there only seemed to be about five taxis in the whole city, and they were only ever at the airport; now a ride is six minutes away, and I’m sure it would be even shorter if I were more centrally located. That’s particularly appreciated in a place like Wisconsin, not only because of the cold, but also the culture of drinking; the reduction in drunk driving alone has long placed Uber solidly on the “societal good” side of the ledger, at least in my book.
Full Self-Driving (Supervised)
Of course I rarely take Ubers: if you’re in the suburbs you drive, and fortunately, I like driving. That’s not the case for everyone, however: while my wife has driven in Taiwan for years, she’s always been nervous about doing the same in America, with its higher speeds, longer distances, and more uncertain directions. That’s why I got her a Tesla: instead of her driving the car, her car drives her.
I’ve actually dawdled in writing this Article because I wanted to try out v14 of Full Self-Driving (Supervised) first, but it’s been over a month since its release and I still don’t have the Update, so my experience is based on v13. That’s ok, though, because Full Self-Driving (Supervised) is actually pretty amazing. It really does go from origin to destination without intervention pretty much all-of-the-time (v14 reportedly addresses the actually leaving the driveway and parking part of things), although I take over more than my wife does.
My issue with Full Self-Driving (Supervised) is two-fold: the first is that it is the absolute best worst driver in the world. What I mean is that Full Self-Driving (Supervised) always handles the situation in front of it with aplomb, including tricky merges, construction, etc. I’m particularly impressed at how it stays with traffic, including speeding when appropriate. That’s the best part. The worst part is that Full Self-Driving (Supervised) seems to have zero planning: it will change lanes even though a turn or an exit is half a mile away, which is particularly galling when an exit lane is backed up; if you don’t take over that leads to an embarrassing attempt to merge back in a quarter mile down the road. In other words, Full Self-Driving (Supervised) gets in more messes than it should because of a lack of foresight, but it handles those messes perfectly. As someone who thinks well ahead of my route in an endless pursuit of efficiency this drives me crazy, but honestly, I would take best worst driver over the vast majority of drivers I encounter on the road.
My second issue is related to why I keep writing out the whole name: the “Supervised” part drives me absolutely batty. Yes, yes, I shouldn’t look at my phone, but is it better to be forced to exit a perfectly competent — more than competent — driving mode to manually steer while sending a text? More galling is when I am looking ahead at a turn — which necessitates turning my head — and get yelled at by my own car to pay attention. I am paying attention, by actually trying to plan more than two steps ahead!
Regardless, I absolutely do believe that Full Self-Driving (Supervised) is good enough to be Unsupervised, at least in good weather; it’s a bummer to realize that that still may not happen for a long time, and even when it does, the price may be things like actually flowing with traffic, even if it’s a few miles over the speed limit. Even then, however, what exists today — and make no mistake, Full Self-Driving (Supervised), with its ability to follow a route, is a step-change from lane-following adaptive cruise control — is enough to make a meaningful difference to someone like my wife. It’s a lot easier to enjoy the big house and yard when you have the capability to go somewhere else.
The Convenience Delta
One challenge I didn’t anticipate was that while trash pickup comes once a week, recycling pick-up is only every other week; that’s a problem given the number of cardboard boxes we go through, mostly from Amazon.
In all seriousness, Amazon has transformed suburban living. It was always the case that the idea of dashing off to the nearby store was more theory than reality, even when I lived downtown; at a minimum I usually still drove. Next day delivery, however, completely changes the mental calculus: the likelihood I will run out of time to go to the store tips the balance towards just ordering what you need the moment you need it; the next day — and sometimes sooner — it’s on your porch (Walmart deserves a callout here: their delivery is usually even faster if you order something in store).
Of course it’s nice to not have to worry about your delivery disappearing, or have to cart it up the stairs or in the elevator; you also have the suburban advantage of having places to store supplies, so you don’t run out in the first place. That was always true though — it’s why big box retailers were very much a product of the suburbs — but marrying that advantage to maximum convenience is a big win.
Food delivery definitely isn’t as good, particularly for the Asian food I sometimes crave; our family has always been one to cook our own food, however, which is of course easier with a big kitchen (and three different types of grills). The better restaurant options are also all downtown, so that’s a minus, but hey, you can always Uber. The broader takeaway is that while there are still certain conveniences that come from a central location, the convenience delta — thanks first and foremost to Amazon — has been dramatically reduced.
Uber’s Robotaxi Risk
There is a point to this diary, and it comes back to Uber. Not only was I a bull during Uber’s rise, I’ve also been fairly optimistic about the company’s fortunes when it comes to robotaxis. From an Update late last year:
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Robotaxis are a technology, not a market — a means, not an end, if you will. Markets are defined by demand, and the demand to be tapped is transportation. And, in this market, the dominant player is Uber; no they don’t have their own robotaxis, but from a consumer perspective, they might as well: the rider doesn’t own the vehicle they ride in, they summon it from an app, and they just walk away when the ride is done. The experience — if not the novelty — is the same with a human driver or a robotaxi.
Moreover, the human drivers come with some big advantages from Uber’s perspective: they bear their own depreciation costs, and can make individual decisions about the marginal rate necessary to provide supply, which is another way of saying that Uber can more easily scale up and down to meet demand by using price as a signal. It is an open question as to whether robotaxis can ever economically scale to meet demand: having enough capacity for peak demand means a lot of robotaxis sitting idle a lot of the time, while maximizing utilization means insufficient supply during peak periods.
This last point is why my assumption is that Uber will very much be relevant in the robotaxi era: their supply network will be essential for scaling up-and-down within cities, and serving all of the areas that the centralized fleets do not. What is less clear is their long-term profitability, which may be somewhat out of their control.
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That last sentence was about Uber’s diminished bargaining vis-à-vis a centralized robotaxi operator versus individual drivers, and it’s an important one in terms of Uber’s long-term valuation. However, as robotaxis continue to expand — Waymo is now in five cities (three via their own service, two via Uber), Tesla (with human supervisors in the car) in two, and Amazon’s Zoox in one — I do wonder if I am making a similar mistake to Horan and Damodaran.
First, like Horan, am I too caught up in the current economics of robotaxis? As an apostle of zero marginal costs I am intrinsically allergic to the depreciation inherent in the cars themselves, along with the significant marginal costs in terms of energy and insurance; Uber side-stepped this by offloading those costs to the drivers. Can scale solve this? At some point — Cybercab already points to this future — vehicles will be purpose-built at scale to be robotaxis, and my experience with Full Self-Driving (Supervised) has me convinced that insurance costs will be manageable, not just because of scale, but because there will be fewer accidents.
Second, like Damodaran, am I limiting my thinking by focusing on the current market — even if that market is already massively larger than the taxi & limo market ever was? The experience of a Waymo is certainly magical; it’s also peaceful, and by removing the human from the equation, provides a sense of safety and security that Uber has always struggled with. This last point could address a major suburban point point, which is kids: the lockdown in kids’ freedom corresponded with a dramatic rise in organized activities, the sheer volume of which leaves lots of parents feeling like unpaid Uber drivers themselves. Some may rely on Uber to solve this problem; it seems likely to me far more would be willing to entrust their children to a Waymo.
That does still leave the peak demand question: even if kids become a major market, what do all of these rapidly depreciating cars do during the day? And thus we arrive at why Amazon acquire Zoox: the obvious answer is delivery. The only thing better than next day delivery is same day delivery; the only thing better than same day delivery is same hour delivery. The best way to make that happen in a cost-effective way is to have a huge number of robotaxis on the road that actually aren’t making the decision that prices aren’t high enough, at least as long as those prices cover the marginal cost of a trip, which, in the case of a robotaxi, includes energy but not a human.
Of course you still have to get the package to the doorstep, which is where robots come in; Tesla is explicitly going in this directions. From The Information:
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Optimus is Tesla’s biggest long-term bet. Musk has said there will eventually be more humanoid robots than cars in the world, and that Optimus will one day be responsible for about 80% of Tesla’s market capitalization. Inside Tesla, he’s pushed the Optimus team to find ways to use the robot in tandem with another big, nearer-term bet: the Cybercab, according to a person with direct knowledge.
That includes Musk’s desire to have the Optimus robot sit in the Cybercab so it can deliver packages. That should be possible: newer versions of the Optimus robot are capable of consistently lifting and moving around with roughly 25-pound objects for three to four hours on a 30 minute charge, another person with direct knowledge said.
But the connection between the robot’s torso and legs isn’t flexible enough to allow it to seamlessly get in and out of a Cybercab, according to the first person. Tesla would need to redesign the robot to change that or use a different vehicle for deliveries more tailored for Optimus’ shape, that person said.
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This is obviously all still a ways out, but it all feels a lot more possible today than it did even a year ago; relatedly, it feels a lot more uncertain that Uber will have a long-term role to play — and the company may agree! I thought this announcement from Nvidia at GTC Washington D.C. was a bearish indicator for the company:
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Nvidia today announced it is partnering with Uber to scale the world’s largest level 4-ready mobility network, using the company’s next-generation robotaxi and autonomous delivery fleets, the new Nvidia Drive AGX Hyperion 10 autonomous vehicle (AV) development platform and Nvidia Drive AV software purpose-built for L4 autonomy.
By enabling faster growth across the level 4 ecosystem, Nvidia can support Uber in scaling its global autonomous fleet to 100,000 vehicles over time, starting in 2027. These vehicles will be developed in collaboration with Nvidia and other Uber ecosystem partners, using Nvidia Drive. Nvidia and Uber are also working together to develop a data factory accelerated by the Nvidia Cosmos world foundation model development platform to curate and process data needed for autonomous vehicle development.
Nvidia Drive AGX Hyperion 10 is a reference production computer and sensor set architecture that makes any vehicle L4-ready. It enables automakers to build cars, trucks and vans equipped with validated hardware and sensors that can host any compatible autonomous-driving software, providing a unified foundation for safe, scalable and AI-defined mobility.
Uber is bringing together human drivers and autonomous vehicles into a single operating network — a unified ride-hailing service including both human and robot drivers. This network, powered by Nvidia Drive AGX Hyperion-ready vehicles and the surrounding AI ecosystem, enables Uber to seamlessly bridge today’s human-driven mobility with the autonomous fleets of tomorrow.
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The thing about Uber the first time around is that it wasn’t simply providing a fancy app for the taxi & limo market; it was providing an entirely new experience for both drivers and riders that was orthogonal to that market, which let it create a far larger one. This deal with Nvidia envisions a different sort of evolution, where Uber’s existing market slowly becomes autonomous; that’s possible, even if it means significantly higher capital costs for Uber (and cars that cost more, since they are retrofitted instead of purpose-built).
What is also possible, however, is that Uber gets Uber-ed: a completely new experience for both drivers (as in they don’t exist) and riders (including kids and packages delivered at the marginal cost of energy) ends up being orthogonal to the Uber market, and far larger. Moreover, this market will, for specific qualitative reasons around safety and security, be inaccessible to Uber’s core business, meaning the entire vision of “bringing together human drivers and autonomous vehicles into a single operating network” ends up being a liability instead of an asset.
The End of Urbanism?
There are larger sociological and political questions around things like urban versus suburban living, just as there were when suburbs were built out in the first place. I do believe that the suburbs are very much back, and not just because I’m back in the suburbs; what will be a fascinating question for historians is the chicken-and-egg one between technology driving this shift, versus benefiting from it.
What is worth considering, however, is if the last wave of urbanism, which started in the 1990s and peaked in the 2010s, might be the last, at least in the United States (Asia and its massive metropolises are another story). The potential physical transformation in transportation and delivery I am talking about is simply completing the story that started with entertainment and television in the first wave of suburbia, and then information and interactivity via the Internet, particularly since COVID. There are real benefits to being in person, just like there are to living in the city, but the relative delta to working remote or living in the suburbs has decreased dramatically; meanwhile, offices and urban living can never match the advantages inherent to working from a big home with a big yard.
Whether or not this is good thing is a separate discussion; I will say it has been good for me, and it’s poised to get even better.
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