I don’t have an us vs. them mentality with regard to Tesla and other companies when it comes to autonomy. I care most about preventing people from dying in car accidents. Two of my friends died that way. It is so heartwrenching to think about them — their plans for the rest of their lives, and how their parents must feel. Car crashes kill about as many people as HIV/AIDS but (unlike HIV/AIDS) we don’t think of road deaths as a crisis because we assume it can’t be solved — we just accept it as part of life, like dying of old age. Self-driving cars offer a new hope. Self-driving cars can also create much-needed economic growth at a time when slow growth is probably contributing to social and political problems, and to a general spirit of pessimism about life and the future. The closer real per capita economic growth gets to 0%, the more that the easiest way to obtain wealth is to take it from someone else — either through political or military means. When it gets to 0%, we get Genghis Khan. More relevant to this era— if we want to stop populism or communism from taking over, we should try to create lots of economic growth and make sure it’s broadly shared. If you are someone concerned about plutocracy, probably the same is true, actually. I believe Thomas Picketty theorized (based on lots of data) that faster real per capita growth reduces wealth inequality, even before government measures to do so. Government measures to redistribute wealth are also probably easier to pass when there is more wealth to go around. Then there’s just the fact that self-driving cars are cool and exciting: they feel like magic to me, and they would make my life so much easier. I hate driving, I’m bad at it (I once hit the same tree twice in one day), but I often can’t get around travelling by car. Visiting family means going down a dirt road. In the city, I actually really enjoy taking the bus when it’s uncrowded and it’s warm enough to have the windows open. It’s serene and nice to be around people. But in the winter at peak hours it often means being sardined up against a throng of irate people while sweating profusely under my winter coat. That kind of experience is what makes some people buy cars. So, I am rooting for Waymo, Cruise, Mobileye, Apollo, Voyage, Aurora, Wayve, and whoever else is working on the problem. If any of them succeed, we all win. We can save lives, create economic growth, and make it way easier and more enjoyable to get around. Make it happen! I also think it’s worth trying a diversity of approaches— throw everything at the wall and see what sticks. Mobileye is trying to decide the car’s actions based on a set of deterministic, hand-coded rules. The critique is that these rules will be brittle. Wayve is taking the exact opposite approach, by doing end-to-end deep learning from perception to action. The critique is that this will result in mean regression, and it will be bad at handling uncommon or unusual situations. Even if I think some approaches are a bit weird or cheesy, no one actually knows what works yet, and it is well worth burning the capital to find out what works. Still, I want to form an opinion about which approach is most likely to succeed. First, just out of plain ol’ curiosity. I love technology. I want to understand how it works and why. I want to get to a deep, first principles understanding and be capable of informed, intelligent original thought on the subject. Second, I want to invest in the opportunity I see coming. I want to make money so I can stop worrying as much about money and have more freedom to focus on what I think is important. I used to feel that finance is somewhat depraved (and still sometimes do), but I’m willing to slog through it so later I can focus on sublime things. I also want my friends and family to herald me as a prophet, and stop giving me such a hard time. So what if I crashed into the same tree twice, and the barbecue, and knocked the porch stairs over. So what if I spill a glass water on myself once a day. My insight paid for those Christian Louboutin sunglasses! There is no inherent reason to pick just one autonomous car company to invest in. Right now, I’m just invested in Tesla, but I would love to diversify if I can find the right investment vehicle. One option is just to invest in a self-driving car ETF comprising dozens of companies. Just spread your bets wide and hope it pays off. That might be a good option. ARK Invest’s Industrial Innovation ETF is one I might consider. However, investing in an actively managed ETF means you probably don’t have the resources to do your own research on all the companies you’re invested in. You’re putting your trust in someone else’s research process. So, for now, I prefer to design my own portfolio. I have been thinking for a while about investing in GM because of Cruise. I like the vertical integration, the tech talent, and how aggressive the company is pushing the tech forward. The main reasons I’m hesitant are: 1) autonomous electric vehicles might cause GM to tank because it’s still so dependent on legacy vehicles and 2) it’s really hard to evaluate what’s legit tech and what is just demoware. If Alphabet were the size of GM or Tesla, I would probably invest because of Waymo. Both GM and Tesla are around $50 billion. That means if self-driving cars are worth $500 billion in market cap, there is the opportunity to 10x. Alphabet’s market cap is $840 billion. Adding $500 billion would only be an increase of 60%. So the opportunity is much smaller just because Alphabet is such a big company. Similarly, Intel is already valued at $220 billion. The opportunity is about 3x instead of 10x. I also don’t know the first thing about the chip business, although I have heard people say Intel’s competitiveness has been receding — which would be a worry. But most of all, the worry I have about Mobileye is not the tech but its business model. It is a supplier to automotive manufacturers. Mobileye’s plan is to sell the hardware and software enabling full autonomy to BMW, Audi, Fiat, Nissan, Honda, etc. That means those cars companies can operate the autonomous ride-hailing services and make virtually all of the money. Mobileye’s bargaining position will probably not be good. BMW, for example, is also partnering with Aurora and Apollo. Aptiv (Delphi) is attempting to compete as a supplier. Waymo has suggested it may be willing to share autonomous ride-hailing revenue with manufacturers. Car companies are increasingly acquiring self-driving startups and starting their own internal self-driving divisions — an attempt to vertically integrate away a supplier like Mobileye. If Mobileye raises its prices too high or demands too large a cut of autonomous ride-hailing revenue, manufactures might turn elsewhere. Tesla ticks all the boxes: ~$50 billion market cap. Vertically integrated: vehicles, software, and even some components. Top-tier tech talent. Software culture. Not dependant on legacy vehicles. Not dependent on uncertain microchip sales. That’s from a business strategy and return on investment point of view. You can see how the company with the most advanced technology isn’t necessarily the best one to invest in. It’s more complicated. Waymo might have the most advanced technology, but it’s part of a giant company. Mobileye might have the most advanced technology, but it’s just a supplier to car companies. Apollo might have the most advanced technology, but it’s an open source project. Android is the most popular OS, but the iPhone makes way more money. The technology is the most uncertain part. This is the area where the least is known (but also the area where people are the most vocal and passionate — ain’t that the way). How do we assess the competitive landscape in terms of technology? Here are a few ideas. Look for direct empirical evidence, like: Demo videos California disengagements data Performance of production systems Statistics shared by companies Assess the resources available to different companies, like: capital (R&D budget) tech talent computing power training data HD map data test cars data labelling employees Second guess the technical decisions made by companies, like: whether to use lidar whether to use end-to-end deep learning whether to control vehicle action using only formalized rules Direct evidence, resources, technical decisions. The one bullet point that stands out to me most is training data. Here’s why. Deep learning is what makes self-driving cars feasible. The obstacles to progress seem to be performing better on tasks that either deep learning already does or that deep learning could potentially take over. (Plus compiling HD maps.) We’re told that the key to making deep learning do things better is more and better data, carefully labelled, cleaned, and curated. That’s supposedly even more important than neural network architecture. So, logically then, self-driving car progress should be a function of the quantity and quality of the data, and how well it is labelled, cleaned, and curated. Does anyone dispute this point? I would love to hear reasons to doubt if this is indeed true. It seems like Tesla has access to the most data. There are around 215,000 Hardware 2 cars on the road. It’s putting about 8,000 more on the road every week. The main limit to the quality and quantity of data Tesla collects seems to be the selection of the conditions that trigger a sensor recording. Can it design triggers that selectively capture plenty of useful data without missing too much of what’s important? Please let me know if you can think of reasons to doubt this — that Tesla can collect the most data that is useful for training neural networks relevant to driving. If it is true that: 1. Progress on autonomy is a function of data. And it is true that: 2. Tesla has the most data. Then it follows that: 3. Tesla is making the most progress on autonomy. This is the logical conclusion I keep coming back to, but I’m very open to the possibility that it’s not correct. I am looking for someone who articulate why this conclusion isn’t sound. Some ideas: lidar is too important (I would like to see more hard data on this) data from testing in autonomous mode is what matters (perhaps for path planning and control?) computing, not data, is what matters (esoteric) HD mapping data is what matters (assumes everything else is already production-ready) AI talent is what matters (needs elaboration) what else?