There are other cool things that come from the de novo model. Outside the car itself, Tesla can sell direct on a fixed price instead of going through dealers. OEM dealers often have contracts around who can install new software (so no remote updates allowed) and those dealers make most of their profits from repairs. Around half of repair spending is on things directly linked to the ICE - no ICE means no oil leaks or broken fan belts. Dealers also play an important role in setting pricing and incentives, and driving demand to specific models. These are all more things that are hard for the incumbent industry to adapt to.

However, again, it’s unclear to me how central these things are. The counter-argument, perhaps, is that that this is comparable to things like the Apple Stores, or the on-device activation of your phone account when you buy an iPhone. These are nice, and a selling point, and hard for Samsung to match, but do we think Apple’s market share would collapse without them?

This is of course very subjective (“how much does this cool thing matter?”), so here’s a thought experiment: if these factors were the only difference between a Tesla and a BMW or Mercedes, and the drive train, acceleration etc were all identical, would they be enough? If BMW suddenly started selling direct and doing seamless OTA firmware uptakes, would Tesla’s share price collapse? Probably not.

Less subjectively, it’s not clear there will be winner takes all effects here. There might be a developer ecosystem on the car itself, but it’s just as likely that the proper place for apps in your car is on your phone, or in the cloud. Certainly, it’s too early to be sure.

Finally, as really should be obvious, there will be chargers everywhere. Once the actual motivation is there, all sorts of companies will build charging stations everywhere they can. The barrier is only capital - there’s no competitive moat here.

Fourth, autonomy

All of this takes us to autonomy. Electric is compelling but will probably be a commodity, whereas Tesla’s improvements on top of electric may not be commodities but are not necessarily decisive. Conversely, autonomy changes the world in profound ways (I wrote about this here), and it’s a fundamentally new technology that doesn’t look at all like a commodity. And Tesla is doing this, too. Sort of.

In most of the previous conversation, I talked about how Tesla as a technology company would or would not disrupt non-tech companies. However, in autonomy, Tesla is not just competing with car companies - it’s competing with other software companies. It doesn’t have to beat Detroit at software - it has to beat all the rest of Silicon Valley at software.

In this competition, Tesla’s thesis is that the data it can collect from its cars will give it a crucial advantage. The only reason that anyone is interested in autonomy today is that the emergence of machine learning (ML) in the last 5 years probably gives us a way to make it work. Machine learning, in turn, is about extracting patterns from large amounts of data, and then matching things against those patterns. So how much data do you have?

Hence, Tesla’s approach to autonomy has been to put as many sensors as possible into the cars it’s already selling, and collect as much data as possible from those sensors. It can do this because its cars are already built on a software platform (as discussed above) - it can ‘just’ add the sensors, in ways that the existing OEMs cannot yet do. Then, as it gets more and more levels of autonomy working, it can push that out over the air to the cars as software updates. Since it already has so many cars with these sensors on the road, this will have a self-reinforcing ‘winner takes all’ effect: it will have more data, and so its autonomy will be better, and so it will sell more cars, get more self-driven miles and so have more data.

If this pays off, it would indeed be a profound and compelling competitive advantage for Tesla, even without thinking about all the other possibilities, such as renting your Tesla out as an autonomous taxi.

However, this is just a thesis, and there are two basic questions underlying it: can we do autonomy with vision alone, and what winner takes all effects apply?

First, vision. The really obvious problem with Tesla’s autonomy plan is that today ‘as many sensors as possible’ means that Tesla is using cameras placed around the car to give a 360 degree view, plus radar that is only forward-facing (and some short-range ultrasonics). This means it must rely on vision alone to get a full 360 degree 3D model of the world around the car.

Unfortunately, computer vision is not yet able to do this well enough. Most people in the field would agree that this will be possible at some point (after all, humans don’t have LIDAR), but it’s not possible yet. Moreover, this isn’t a question of just adding more data and getting vision to work by brute force (or at least, we don’t know that it is). This is why pretty much everyone else is using vision combined with multiple LIDAR sensors and often multiple radar units as well. Today, that adds tens of thousands of dollars of cost to each vehicle. If you’re only running an engineering testing and development fleet of at most a few dozen or hundred vehicles, this is bearable, but it’s clearly not possible to add this to every new Tesla Model 3 - the sensors would cost more than the car. (There’s also the issue that you have to add bulky, fragile and impractical lumps all over the car.) The cost and size of these sensors is falling fast (for example, there is a race to get the first solid-state LIDAR working), but we are still some years away from their being cheap enough to put on a production car.

But meanwhile, even if you can create an accurate 3D model of the world around the car, with whatever sensors you like, the rest of the autonomous puzzle isn’t working yet for anyone, nor does anyone in the field think that this is close. Bits of it work quite well - cruise control on highways, say - but the whole does not.

Hence, Tesla’s first bet is that it will solve the problem of building a model of your surroundings using only vision before the other sensors get small and cheap, and that it will solve all the rest of the autonomy problems by then as well. This is strongly counter-consensus. It hopes to do it the harder way before anyone else does it the easier way. That is, it’s entirely possible that Waymo, or someone else, gets autonomy to work in 202x with a $1,000 or $2,000 LIDAR and vision sensor suite and Tesla still doesn’t have it working with vision alone. It’s also possible that everyone gets sensing working perfectly with vision alone and the rest of autonomy still doesn’t work for anyone.

The second bet is that Tesla will be able to get autonomy working with enough of a lead to benefit from a strong winner-takes-all effect - ‘more cars means more data means better autonomy means more cars’. After all, even if Tesla did get the vision-only approach working, it doesn’t necessarily follow that no-one else would. Hence, the bet is that autonomous capability will not be a commodity.

This takes us back to the data. Tesla clearly has an asset in the data it can collect from the 200k+ Autopilot 2 cars it’s already sold. On the other hand, Waymo’s cars have driven 8m miles, doubling in the last year or so. Tesla’s cars have driven more (without LIDAR, but set that aside). But how much do you need?

This is really a question about all machine learning projects: at what point are there diminishing returns as you add more data, and how many people can get that amount of data? It does seem as though there should be a ceiling for autonomy - if a car can drive in Naples for a year without ever getting confused, how much more is there to improve? At some point you’re effectively finished. So, how many cars gathering data do you need before your autonomy is as good as the best on the market? How many companies might be able to reach that? Is this 100 or a thousand cars driving for a year, or 1 million cars? And meanwhile, machine learning itself is changing quickly - one cannot rule out the possibility that the amount of data you need might shrink dramatically.

So: it’s possible that Tesla gets the vision-only approach to work, and gets the rest of autonomy working as well, and its data and its fleet makes it hard for anyone else to catch up for years. But it’s also possible that Waymo gets this working and decides to sell it to everyone. It’s possible that by the time this starts to go mainstream, five or ten companies get it working, and autonomy looks more like ABS than it looks like x86 or Windows. It’s possible that Elon Musk’s assertion that it should work with vision alone is correct, and 10 other companies then get it working.

All of these are possible, but, to repeat, this answer is not a question of disruption, and this is not a matter of whether software people will beat non-software people - these are all software people.

🤔

This post began as a much shorter piece about Tesla and Netflix, comparing two companies that are using software to change other industries. But the fascinating thing about Tesla is that there are so many different things going on, and so many different kinds of innovation. I’m sure I’ve missed plenty of things. One of the issues that recurs in thinking about Tesla is that tech people don’t really know enough about cars, and car people don’t really know enough about software.

But the history of the tech industry is full of companies where having a lovely product, or being the first to see or build the future, were not enough. Indeed, the car industry is the same - a great, innovative car and a great car company are not the same thing. Tesla owners love their cars. I loved my Palm V, and my Nokia Lumia, and my father loved his Saab 9000. But being first isn’t enough, and having a great product isn’t enough.