Skip to main content

What the car industry and wallstreet get wrong about Tesla




Every time I watch a car review that includes a Tesla somewhere, it is always fascinating how the reviewers react. They try hard to compartmentalize the Tesla into their existing matrix and scorecards of what makes a good car. I sometimes roll my eyes when the subject of materials, build quality and range come up. Obviously, these things matter for car people - but what should also be discussed and what reviewers generally do not grasp is that Tesla changed forever the way we look at cars. Tesla cars are really computers with wheels whereas all the other electric cars, from the incumbent car makers, are just cars.

Why is this important? 

It changes everything - it’s the same comparison between a feature phone and the iPhone. The feature phone was primarily used to make phone calls and send the occasional text. The iPhone became a platform business where a whole new economy was created through the power of software.

Just like Apple, Tesla pursues a single-software-stack approach and by that I mean that Tesla controls every aspect of its software and thus can release new operating systems and new features at will. Compare this with an incumbent car manufacture where most components are being sourced from different companies that use their own software stack and sdk (software development kit) which then need to be painfully integrated into a single, monolithic clump of software.
This has been the model of choice for most industries and is only now changing thanks in part of disruptors with an Api first and software first approach. I’ve covered this topic in my previous API first article.

Now, back to Tesla: you’re driving a computer (or rather the computer really drives you). If we think about the implications of this approach, it becomes immediately clear that:
a)    Tesla pursues a new approach to building cars by using new technology and new ideas
b)    Incumbent car manufacturers are double-whammied

Obviously, with a) I simply state the fact that Tesla is a disruptor just like Revolut is for the banks, Apple was for Nokia, and photo sensor for Kodak - that’s just the cycle of technology obsolescence (in this case, the combustion engine) and technological awareness and best use. And it’s a natural thing that constantly happens. But, we can’t overlook the fact that we actually need to do this for our and our next generations sake. We are still driving on dead Dinosaurs “carcasses”and it’s only because we didn’t have the right infrastructure in place when the car industry started to ramp up. Nobody was thinking about the ramifications of extracting, transporting, refining and combusting dead dinosaurs. So this has to change - and that’s why the next point is so difficult to comprehend:

With B) Tesla has given two challenges for the incumbent car industry that they could have solved by themselves a long time ago:
  1. The electric drive train forced Tesla to create its own components. They have had mass production cars in circulation since 2012. In short, Tesla is years ahead. I’m not just talking about putting batteries in a car and using an electric motor, I’m also talking about the whole infrastructure that requires charging stations (read about our trip through France: here) and sophisticated software to manage it all.
  2. The aforementioned software first approach that requires a completely new way of building cars. You have to start making software for each of your components. Tesla is estimated to be around 80% vertically integrated. That’s how much you have to control in order to create an experience and platform business that Tesla has created.

This combination is very powerful, not only does it lead to a platform business it also enables completely new use cases and functions. Case in point, Tesla Arcade, Tesla’s Dog mode, Tesla’s Sentry mode, Autopilot, The summons mode and of course, full self driving (>3 hours long video). But think about the future of this system, what does it mean for insurance policies when I can clearly show them that someone scratched my car, or think about the accident avoidance. What about the data that every Tesla car collects from the street: several cameras are constantly recording, I suspect every City would be interested in having this data to better pin-point accidents or choke points. There is certainly commercial use for that data. Tracking pedestrian traffic, anyone? In the future, I would expect third parties to make software for the Tesla “computer on wheels” and Tesla will most likely have its own app store so that these apps can tap into all that data that Tesla’s connect.

Which brings me to this realization: the car industry is doomed. They haven’t realized yet but unless they are disrupting themselves NOW, most of the big players will either be gone, or reduced to a shell of their former selves. Of course, this development will happen over a 20 year time horizon but given all the other troubles the car industry has, it might just be the last nail in their coffins. This is also why I think Tesla just might be the most important company on the planet (next to Space X and maybe the GAFAs) because it will touch so many aspects of our lives. Not only is it dragging along the whole car- and power industry, it’s also helping us to get serious about CO2 emissions. And yet, Wall Street analysts treat Tesla as a car manufacturer and they use those methodologies to try and put a value on it - not that different from what the car reviewers are doing. I think both are missing the bigger picture.


Comments

Popular posts from this blog

Will smart phone cameras be better than digital mirrorless cameras?

  If you believe Terushi Shimizu or rather, the way the press is formulating it , then camera phones will have better image quality in 2024 than your trusty DSLR or mirrorless digital camera. He backs this up with sensor technology advancements and computational photography. He has a point.     However, as a digital camera enthusiast myself, I must strongly disagree with this point of view. The message might be interpreted in such way that its meaning reflects a view that we are no longer bound by physics to get the best image quality.     The thing is this, the bigger your camera sensor, the more photons it can capture. However, this comes at the realization that big sensors require big lenses which in turn makes the camera big and heavy. I’m simplifying of course, but that’s physics. For camera makers it is therefore always a question of tradeoffs: do you want better image quality or do you want a smaller and lighter camera. Camera phones or cameras in smartphones, have changed this

Apples Vision Pro Headset strategy is all about its Arm-chips.

  Apple has given us a vision of what their VR and AR future might entail. But as have others pointed out numerous times, the whole point of the showcase at the WWDC 23 was to let people experiment, I’ve heard others say that it’s like the launch of the Apple Watch when Apple didn’t really know what would become of it. This is similar and yet different.  Just like the Apple Watch (and the iPad before it), Apple sought to porting its whole ecosystem onto a watch – granted, the Apple Watch can’t live on its own and a better comparison would probably be the iPad. The iPad can live without any other Apple device and unlike the iPhone, never really had a clearly defined function other than to watch movies and browse the web. It was not until it gained the ability to be used with a pencil that artists and designers started to explore the potential.  I’m trying to point out that Apple took 5 years from the first iPad in 2010 to the iPad Pro with pencil in 2015 to find its “killer-app”. But th

The new shiny armor of AI

If we listen to the media, business leaders, and the press, we should be getting behind the AI wagon because of its potential to automate many of the processes everyday companies struggle with. I don’t dismiss this notion entirely because I think it’s true if you have the ability to integrate this technology in a meaningful way. For example, the startup company " scrambl " (full disclosure, I’m a minority investor) is making use of gen-AI by "understanding" CVs (curriculum vitae) from applicants and identifying the skills to match them to open positions. This works great – I have seen this in action, and while there are some misses, most of that "normalization of skills" works. There are other promising examples, such as Q&A systems to understand the documentation of a complex environment. When combined with RAG ( retrieval augmented generation ), this has the potential to significantly reduce the time it takes to make complexities understandable. But