This doesn't just hold true for regular employees either, but also for executives. Tesla has been getting a lot of hate over the past few years for supposedly having high executive turn-over, but I have no doubt that the majority of executives who leave Tesla either do so because they don't want to work so hard, or because Elon is dissatisfied with them. Ask yourself, if you were a finance prodigy like Zach Kirkhorn , and recently became Tesla's new CFO after working your way up through the ranks over the past six years, where else are you going to do more exciting work than at Tesla, the company that is growing their revenues 10-fold over a 5-year periods, and that is on track to become the largest company in the world in the next decade? Where else are you going to make more in stock options as a result? Where else is your work going to be more meaningful? And where else are you going to work with somebody like Elon Musk? I think the same holds true for all the other executives at Tesla, such as Andrej Karpathy, Jerome Guillen, and Franz von Holzhausen. Not to say that none of them could leave for any reason whatsoever, but I don't think any of them can find more challenging and rewarding work than they are currently doing at Tesla.

Now that we've established Tesla's most important competitive advantages, let's take a look at what Elon is planning to use them for in the next decade. Let's take a brief look at what he's promised us through his Master Plan, Part Deux . Below is copied Tesla's Master Plan, Part Deux, with some comments from me in

Create a smoothly integrated and beautiful solar-roof-with-battery product that just works, empowering the individual as their own utility, and then scale that throughout the world. One ordering experience, one installation, one service contact, one phone app.

By definition, we must at some point achieve a sustainable energy economy or we will run out of fossil fuels to burn and civilization will collapse. Given that we must get off fossil fuels anyway and that virtually all scientists agree that dramatically increasing atmospheric and oceanic carbon levels is insane, the faster we achieve sustainability, the better.

However, the main reason was to explain how our actions fit into a larger picture, so that they would seem less random. The point of all this was, and remains, accelerating the advent of sustainable energy, so that we can imagine far into the future and life is still good. That's what "sustainable" means. It's not some silly, hippy thing -- it matters for everyone.

Part of the reason I wrote the first master plan was to defend against the inevitable attacks Tesla would face accusing us of just caring about making cars for rich people, implying that we felt there was a shortage of sports car companies or some other bizarre rationale. Unfortunately, the blog didn't stop countless attack articles on exactly these grounds, so it pretty much completely failed that objective.

Also, a low volume car means a much smaller, simpler factory, albeit with most things done by hand. Without economies of scale, anything we built would be expensive, whether it was an economy sedan or a sports car. While at least some people would be prepared to pay a high price for a sports car, no one was going to pay $100k for an electric Honda Civic, no matter how cool it looked.

The reason we had to start off with step 1 was that it was all I could afford to do with what I made from PayPal. I thought our chances of success were so low that I didn't want to risk anyone's funds in the beginning but my own. The list of successful car company startups is short. As of 2016, the number of American car companies that haven't gone bankrupt is a grand total of two: Ford and Tesla. Starting a car company is idiotic and an electric car company is idiocy squared.

Alright, so the first promise from Master Plan, Part Deux, is to provide customers with an integrated solar and energy storage solution, and to then scale that throughout the world.

Today, Tesla addresses two relatively small segments of premium sedans and SUVs. With the Model 3, a future compact SUV and a new kind of pickup truck, we plan to address most of the consumer market. A lower cost vehicle than the Model 3 is unlikely to be necessary, because of the third part of the plan described below.

We can't do this well if Tesla and SolarCity are different companies, which is why we need to combine and break down the barriers inherent to being separate companies. That they are separate at all, despite similar origins and pursuit of the same overarching goal of sustainable energy, is largely an accident of history. Now that Tesla is ready to scale Powerwall and SolarCity is ready to provide highly differentiated solar, the time has come to bring them together.

So Tesla also plans to address the rest of the automotive market, mostly through the Model Y and Cybertruck.

What really matters to accelerate a sustainable future is being able to scale up production volume as quickly as possible. That is why Tesla engineering has transitioned to focus heavily on designing the machine that makes the machine -- turning the factory itself into a product. A first principles physics analysis of automotive production suggests that somewhere between a 5 to 10 fold improvement is achievable by version 3 on a roughly 2 year iteration cycle. The first Model 3 factory machine should be thought of as version 0.5, with version 1.0 probably in 2018.

Ramp up production as quickly as possible through major manufacturing improvements, check.

In addition to consumer vehicles, there are two other types of electric vehicle needed: heavy-duty trucks and high passenger-density urban transport. Both are in the early stages of development at Tesla and should be ready for unveiling next year. We believe the Tesla Semi will deliver a substantial reduction in the cost of cargo transport, while increasing safety and making it really fun to operate.

Sell not just passenger vehicles, but also cargo vehicles starting with the Tesla Semi.

Re-imagine public transportation for dense urban areas.

With the advent of autonomy, it will probably make sense to shrink the size of buses and transition the role of bus driver to that of fleet manager. Traffic congestion would improve due to increased passenger areal density by eliminating the center aisle and putting seats where there are currently entryways, and matching acceleration and braking to other vehicles, thus avoiding the inertial impedance to smooth traffic flow of traditional heavy buses. It would also take people all the way to their destination. Fixed summon buttons at existing bus stops would serve those who don't have a phone. Design accommodates wheelchairs, strollers and bikes.

I don't remember exactly when, but I know Elon commented on buses at some point during a conference call a few years ago. He said that he no longer believes that buses will be useful, because autonomy will be here soon and make them far less useful.

It is also important to explain why we refer to Autopilot as "beta". This is not beta software in any normal sense of the word. Every release goes through extensive internal validation before it reaches any customers. It is called beta in order to decrease complacency and indicate that it will continue to improve (Autopilot is always off by default). Once we get to the point where Autopilot is approximately 10 times safer than the US vehicle average, the beta label will be removed.

According to the recently released 2015 NHTSA report, automotive fatalities increased by 8% to one death every 89 million miles. Autopilot miles will soon exceed twice that number and the system gets better every day. It would no more make sense to disable Tesla's Autopilot, as some have called for, than it would to disable autopilot in aircraft, after which our system is named.

I should add a note here to explain why Tesla is deploying partial autonomy now, rather than waiting until some point in the future. The most important reason is that, when used correctly, it is already significantly safer than a person driving by themselves and it would therefore be morally reprehensible to delay release simply for fear of bad press or some mercantile calculation of legal liability.

Even once the software is highly refined and far better than the average human driver, there will still be a significant time gap, varying widely by jurisdiction, before true self-driving is approved by regulators. We expect that worldwide regulatory approval will require something on the order of 6 billion miles (10 billion km). Current fleet learning is happening at just over 3 million miles (5 million km) per day.

As the technology matures, all Tesla vehicles will have the hardware necessary to be fully self-driving with fail-operational capability, meaning that any given system in the car could break and your car will still drive itself safely. It is important to emphasize that refinement and validation of the software will take much longer than putting in place the cameras, radar, sonar and computing hardware.

Develop software that can drive a car autonomously 10 times safer than a human. That sounds pretty useful.

You will also be able to add your car to the Tesla shared fleet just by tapping a button on the Tesla phone app and have it generate income for you while you're at work or on vacation, significantly offsetting and at times potentially exceeding the monthly loan or lease cost. This dramatically lowers the true cost of ownership to the point where almost anyone could own a Tesla. Since most cars are only in use by their owner for 5% to 10% of the day, the fundamental economic utility of a true self-driving car is likely to be several times that of a car which is not.

When true self-driving is approved by regulators, it will mean that you will be able to summon your Tesla from pretty much anywhere. Once it picks you up, you will be able to sleep, read or do anything else enroute to your destination.

Launch a ride-hailing service through which customers can have their cars earn money for them.

In cities where demand exceeds the supply of customer-owned cars, Tesla will operate its own fleet, ensuring you can always hail a ride from us no matter where you are.

Operate a fleet of robotaxis.

But there is one more promise somewhat hidden in Master Plan, Part Deux, and it lies in the "Terrestrial Transport":

Develop, manufacture, and sell environmentally friendly boats and airplanes.

So now that we have an idea of where Elon wants to take Tesla, let's see if TLSA hitting $420 yesterday simply got me high on this company's potential, or if Elon truly has a chance to monopolize some of these industries with Tesla, like he's almost monopolized the commercial launch services industry with SpaceX.





Tesla Energy

When I wrote my original Tesla Investment Thesis , I wasn't very optimistic about Tesla's energy division, and I mostly wrote it off as an "I'll believe it when I see it" type of thing. However, I've come to change my mind a little bit, and I am now cautiously optimistic about Tesla Energy. It remains the part of Tesla's business I am least familiar with though, so this section will require me to do the most research and will be the most difficult to write. Therefore, let's start with the easiest part.





Energy Storage

We've already established that one of Tesla's biggest competitive advantages is the fact that it's the world leader in (lithium-ion) battery technology, in part because its energy storage products are cheaper on a $/kWh basis than any of its competitors by a large margin. And this is before further advancements are announced at next year's Battery & Powertrain Investor Day.













https://hypercharts.co/tsla

(Q1'18 was unusually large because of the project in South Australia)

So if Tesla's batteries are so much better and cheaper than its competitors, then why does Tesla currently not hold a monopoly in the energy storage market? The answer lies in a lack of supply. Tesla currently simply cannot even produce enough batteries to fulfill the demand for its vehicles , and has said that it had to convert production lines that were meant for energy storage production to Model 3 battery pack production. But things are looking up a little bit for Tesla's energy storage business, because MWh deployed has once again started increasing this year.

According to this Bloomberg forecast , the global energy storage market is expected to increase 122x from 17GWh in 2018 to 2,850 GWh in 2040, so there is an enormous business opportunity for Tesla here. Considering Tesla's lead in batteries, and presuming Tesla's world-class engineers and supply chain managers are going to be able to scale battery production rapidly, this is looking very good, but we'll have more clarity after the upcoming Battery & Powertrain Investor Day.





What could go wrong?

Assuming Tesla maintains its large lead in batteries and its employees are able to scale production, it's just a matter of time before Tesla dominates the energy storage market. However, over the course of 10-20 years, there's a higher possibility that a third party will discover a massive battery breakthrough. But as I mentioned, in all likelihood they would take this technology to the world's largest consumer aka Tesla.





Energy Generation - Solar Roof

When I woke up this morning, I had no idea about the competitive landscape of Tesla's Solar Roof, except for that I knew it'd be competing against regular roofs and regular rooftop solar. Little did I know that Tesla is not the only company who offers a roof product that can generate energy through photovoltaics. Tesla will have to go up against a number of competitors in the BIPV (Building Integrated Photovoltaics) market as it is called. Let's take a look at who these competitors are, and what Tesla's chances are of beating them.





CertainTeed's Apollo II Tile

This is the most well established BIPV product on the market. The biggest drawback is that they only sell solar tiles, and no non-solar tiles. This means that you'll either pay much more for a roof that produces more energy than you need, or you'll have to go through the hassle of only installing the Apollo II tiles on part of your roof, and covering the rest of it with regular roof tiles. If you choose to go for the latter, you'll also end up with a roof looking like this:





https://www.certainteed.com/solar/products/apollo-tile-ii/

Not terrible looking, but a bit funky.







Luma Solar Roof

Luma is a much smaller private company offering a Solar Roof product similar to Tesla's. Unlike CertainTeed, Luma also offers non-solar tiles, so you don't have to mix their product into a conventional roof, and you'll end up with a sleek looking monotone roof.









The problem with this roof however, is its price. Reportedly custom made for $4.00 per watt , this roof is at the very high end even for a premium product.





RGS Powerhouse 3.0





The Powerhouse is a BIPV product originally developed by a different company, but was sold to RGS Energy in 2017, and its price is $3.30 per watt. The largest problem with this product is the financial stability of the company offering it.









Over the course of just 5 years, the company's market cap has dropped from 180M to a meager 8M, and it has delisted from the Nasdaq. It has exited its traditional solar business and is currently exploring "strategic alternatives" . This isn't the type of company you want to be reliant on to service and repair your solar roof over the next few decades.





SunTegra Tile





Like most of these products, SunTegra only sells solar tiles and thus require integration with regular roof tiles. The result doesn't look what I'd call S3XY. The price is reportedly around $3.65 per watt, which is pretty expensive.





Exasun X-Roof

It is anybody's guess whether Tesla will ever acquire TBC, but even if that never happens it's likely that a successful TBC will benefit Tesla. TBC is already leveraging Tesla's EV technologies for their Loop system in which AEVs made of a modified Tesla Model X chassis will be used to transport passengers . It looks like TBC will dig and construct the tunnels, and Tesla will provide all the vehicles. TBC will also need to charge the batteries of these AEVs, so will presumably also leverage Tesla's Supercharger technology in some way.





Elon Musk's reputation should already lend TBC a large amount of credibility, but it seems like this is not the case everywhere like in Chicago. If TBC manages to successfully complete one or two projects though, this should really help them to garner interest from major cities across the world, because public transportation infrastructure is usually extremely expensive to build, but TBC promises to do it a fraction of the cost, similar to how SpaceX has drastically reduced the cost of access to space. Tesla would also benefit to a lesser extent from TBC's success.





The chances of TBC's success are hard to estimate at this point. If Elon wasn't so busy running SpaceX and Tesla (and Neuralink), I'd be confident that TBC would do some great things in the next decade based off of Elon's track-record and leadership alone. It's some really cool stuff they're doing, but even though I'm a Tesla investor with a very long investment horizon, I don't see TBC materially impacting my Tesla investment within that horizon. Assuming things go well, this looks like it has the potential to get exciting sometime in the 2030s and really transform the world in the 2040s, but for now I'm going to focus on other parts of Tesla's business.





What could go right/wrong?

Many things could go wrong. Elon's a bad-ass engineer and entrepreneur, but he is unable to dedicate a lot of time to TBC. Not to say that the people who are working on it are slouches, but they don't have Elon's track-record and startups fail more often than not. In essence, a lot of things could go wrong. On the other hand, it is entirely possible that the Las Vegas Loop will be operational in 2020, and that the city is so excited about the project that it approves the expansion plans throughout its city center. The same thing could happen in LA, and if TBC really achieves the cost reductions they are talking about, LA and LV could become a model for a new form of transportation that cities and countries around the world will be lining up for in the mid and late 2020s. Loop and Hyperloop systems in a lot of major metropolitan areas in the 2030s would be a hell of an exciting world, so let's root for the latter to happen.





Automotive Summary

This last public transportation section didn't end up being strictly automotive, but the vehicles going through the tunnels kind of are based on autos, so whatever. Anyway, we've found out that the potential of Tesla's main business is pretty damn enormous. Like I said, I think Tesla is effectively guaranteed to be the market leader if they continue to execute, and has a lot of potential to well exceed that and capture a majority market share, perhaps even (near-)monopolize this industry. What kind of financial implications this has for Tesla, and what kind of effect it could have on TSLA stock price, we'll take a look at later. First let's move on to another industry that Tesla is extremely well positioned in.





Autonomy

In the case of Tesla, its autonomy business and future ride-hailing business, Tesla Network, are intertwined and often talked about as if they are the same. Many competitors however, are aiming to compete in only one of these industries. Therefore, I'm going to talk exclusively in this section about the business that is the software and hardware that turn an ordinary vehicle into a fully self-driving vehicle. I will cover the ride-sharing business in the section after this.





Solving Autonomy

For a computer to drive a vehicle there are two key skills it needs to master: Perception and Planning. Perception is about perceiving the world around the vehicle correctly. Identifying other vehicles, pedestrians, cyclists, lane markings, traffic lights, a lot of other things, and apparently even garbage cans.





(7:26 for the garbage cans)





Planning is about using the image of the world around the vehicle from Perception, to plan how to correctly navigate through that world. In simpler words, an FSD system has to 'perceive' the world around it, and then use that information to 'plan' a route through it.





This is a slight oversimplification of an entire FSD system, because this paper explains that car navigation and car control are also part of the equation, and Nvidia's self-driving cars website hints that localization (determining the vehicle's location in the perceived world) sits in between perception and path planning.





However, car navigation is a solved problem, and we never hear about AVs (autonomous vehicles) failing because they took the wrong exit off of the highway, so this should not be holding us back from already having AVs right now. Car control does also not seem like it's holding us back. There have been great cruise control systems around for a while now, proving that controlling vehicle speed is not an issue, and there are now also a number of good lane keeping assist systems, proving that vehicle steering is also something that computers can do well. The two most difficult problems to solve are Perception and Planning.





Perception

Humans use a combination of vision and sound to perceive the world around us when we are driving. Various companies have come up with different ways for computers to perceive the world around the vehicle. Here's a brief overview of the common sensors used in the development of AVs:

Cameras. These are a must. I'm not aware of anybody trying to develop AVs without cameras, and considering that our entire road transportation system is meant to be navigated with vision (traffic lights, traffic signs, etc.), I don't see how this could be possible. Radar. Radar sensors help to measure distances. They work well in adverse weather conditions, and Tesla is even using them to sense objects that cannot be seen directly, by bouncing radar beams underneath cars to sense what is happening in front of other vehicles. LIDAR. LIDAR is a little bit like radar in the sense that it detects the distance of objects. The differences are that LIDAR generally is used to detect objects in 360 degrees around the vehicle, is much more expensive, and does not work in bad weather conditions. Ultrasonics. These are similar to radar and LIDAR in that they are used to measure distances, but they tend to be cheaper and much shorter range. The cameras are a must-have, because they are the only way to understand certain vital information such as traffic lights and traffic signs. To go from images and video footage to a representation of the world around a vehicle, companies use the science of Computer Vision . Through Machine Learning and with the help of Neural Networks we help computer to recognize objects (such as cars, pedestrians, traffic lights, etc.) with which the computer can create a virtual representation of the world around the vehicle. For the people among us that don't know how Machine Learning and Neural Networks work, a very simple explanation of how we teach a computer to recognize objects is as follows. We gather a very large amount of images of a type of object, say pedestrians, that we want to teach a computer to recognize. We translate these images into sets of numbers that the computer can understand. We create a mathematical set of computations that the computer performs on these numbers, and that will lead to a certain output of 1 in the case that the image is of a pedestrian, and 0 in the case that the image is not of a pedestrian. At first these computations, and therefore their outcomes, are completely random, and the computer will not be able to recognize pedestrians correctly. However, we then keep showing the computer images of pedestrians, and every time it is incorrect we slightly adjust the computations the computer makes on the images/numbers, so that it will correctly identify the image the next time. Exactly how to adjust the computations, is what the science of Machine Learning and Neural Networks is about. Over time, as the computer corrects the computations more and more to correct for mistakes it is making, it'll start to make less and less mistakes, because it'll have seen a large amount of images and have a good idea of what all types of pedestrians look like. You can sort of think of it as having a number of points, and a computer trying to draw a line that goes through all these points. Kind of like happens in this video from 0:15 to 0:20 and from 1:24 to 1:30.





This is a very simple explanation of how a computer can learn to identify objects around a vehicle. When it comes to Perception for the purpose of creating an AV, we need more than just knowing what kind of objects are around the vehicle and in which direction they currently are. In many cases we also need to know the distance of these objects, and we need to understand if they are stationary or moving, and if they are moving, at what speed.









The final pieces of the perception puzzle (detecting movement and speed of movement) can be calculated based off of difference between the detected objects, as long as the computer knows the distance of those objects. These are relatively simple as long as the detection of objects and measurements of the distance of objects are accurate.





Planning

At this point you might think that the rest is relatively simple. After all, if a computer accurately understands the world around a vehicle, it should be able to figure out how to navigate through that world, right? Wrong. If you find self-driving vehicles fascinating and have not listened to Lex Fridman's interview with George Hotz yet, I highly recommend listening to the full two hours. It's super interesting.





53:10 - 58:37





However, if you don't have that much time to spare, I recommend listening from 53:10 to 58:37 at least to hear him explain how complicated the Perception and Planning problems are, and that Perception and Planning cannot be separated from one another. It's quite complicated and technical, but what it comes down to is that the world around a vehicle is too complex for a human to describe in concrete terms. Things such as occlusion (objects hidden behind a bush, around a corner, or behind another vehicle) mean that you can't just make a list of all objects and their locations. There are also more abstract pieces of information that we humans instinctively use to drive cars, and therefore a self-driving computer's Perception layer can't simply pass on a list of objects and their directions, distances, movements, etc. to the Planning layer. In George's opinion, the Perception layer has to pass on a more abstract representation (a 1024 dimension vector in the case of his company), and the Planning layer has to learn how to use this vector filled with abstract information to learn how to drive. Any system that leaves out the more abstract information, is not 100% accurate and is missing information that is vital to safely drive a vehicle. According to him GM's Cruise and Waymo are not factoring in this abstract information in their planning systems.





Whether Perception or Planning is the more difficult problem to solve, I am not sure, but these are the two systems that need to be improved to the point where they make mistakes less often than humans do. When somebody reaches that point, they will have successfully developed a self-driving car.





One last thing I want to mention before we move on, is that there is another reason Elon is very outspoken against using LIDAR. The reason Elon thinks LIDAR is a crutch, is that most companies using LIDAR rely on it heavily, even though it's not what is going to get a system to become safer than a human driver. It only helps to measure distances, but the final 9s that have to be added to the 99.999999999% safety required to be safer than a human are going to have to be added through vision. Just think about what kind of situations you would expect a very safe, but not as safe as a human AV to trip up over. The situations that an AV will make mistakes in as it gets safer and safer, will be increasingly rare edge cases of objects and situations that are almost never encountered on the road. Cars flipped over, exotic animals on the road, and objects falling off of bridges are likely to be among these rare edge cases. The system will have to recognize first and foremost what these objects are, and for that you need a bad-ass computer vision system rather than LIDAR.





Iteration

The next step required to create an FSD system is iteration, because you need it to be safer than human drivers before you can gain regulatory approval. To be able to iterate on your system and improve it, you need two things:

Large scale testing. You need a way to find out where your system makes mistakes and needs to be improved. Both the real world and simulators are used for this purpose. Initially it will be easy to find mistakes, but as a system gets better and better, you need to test at a larger and larger scale to discover new edge cases that your system trips up over. Large amounts of data. In order to fix the mistakes the system is making, you need large amounts of data of similar instances. For example, if the system made a mistake and the cause was determined to be due to the incorrect detection of a vehicle that was sliding sideways, you need a lot of examples of sideways sliding vehicles to teach your system to correctly detect sideways sliding vehicles in the future. Another on-going debate is that of real-world data vs simulators. I'm not an FSD engineer, and although I haven't heard anyone say that simulators don't serve any purpose, I'm unsure what purpose they do serve. My guess is that it has something to do with generating supplemental data of situations that have already been encountered in the real world. Say your system messes up because of that sideways sliding vehicle, you might be able to create some extra examples of sideways sliding vehicles in a simulator.

What I do know however, is that simulators are not a substitute to real world large scale testing. The world and all possible driving situations are simply FAR too complex to simulate. On the road to full autonomy companies will come across thousands, if not millions, of edge cases that they could've never imagined, and that they could've never accurately simulated. Just like George Hotz argued that you can't make a list of things to accurately represent the world around a vehicle, you can't accurately represent everything about the world around a vehicle in a simulator. And like Elon has said, if you know in which situations your system is going to make mistakes, you don't need to simulate them to fix them.





Hardware

So now you know how to develop the software part of an FSD system, but software needs to run off of hardware, and in the case of an FSD system that hardware needs to have certain requirements. A super computer in a server room in some building may have access to all the power it requires, a computer in an AEV is going to have access to limited power if you want the vehicle to be able to travel meaningful distances. The battery capacity is limited after all.





Furthermore, all the hardware in the system (that includes sensors) will have to be economically viable. This is why Waymo started developing their own LIDARs in house to reduce costs, because the almost $100k sticker price of LIDAR systems was prohibitive to the economic feasibility of their business plan.





Regulatory Approval

So let's assume that you've gotten this far. You've solved Perception and Planning by Iterating on your system until it could drive a vehicle from point A to point B safer than a human is capable of, and you've developed hardware capable of efficiently running the system. Next, you'll want to sell this system and get mad rich, but there is one more hurdle you will have to overcome, which is regulatory approval. Nobody is going to buy your system, unless they are allowed to use it. It'd be kind of like selling medicine that is not approved by the FDA, or like selling a car that is not road legal. You can use that car on a track, and you could use an FSD system on private property, but the market for that is probably not very big.





It's hard to say exactly how this will play out, and different jurisdictions will likely handle things differently. The EU has already been giving Tesla a hard time over its semi-autonomous features . They've been slow to approve things, and they've set very strict guidelines that Tesla's software must abide by. However, when it comes to getting approval for full self-driving, one thing is for certain. Data will be king.





Imagine being a regulator and being asked to approve an FSD system. Your main concern is going to be safety, and minimizing the lives lost on the road each year. Some people also argue that it's unacceptable if people are killed by computers, but would you rather have 50.000 people die from traffic accidents in 2020 from human errors, or would you rather have 25.000 people die from traffic accidents in 2020 from computer errors? If you had to undergo a medical operation and you had to choose between a computer surgeon with a success rate of 50%, or a human surgeon with a success rate of 25%, which one would you choose? I think that as long as the increased safety of the computer can be proven beyond any reasonable doubt, people will choose the computer surgeon, and regulators will choose to approve an FSD system.









To prove beyond a doubt that your system saves lives, you'll have to prove that it will cause less fatalities than the average human in the safest countries on earth, that appear to record about 3.5 deaths per 1 billion miles. To prove this, you're going to need to not only develop a system safer than this, but you're going to have to show proof of it over tens of billions of miles. If you manage to do that, you should be able to get regulatory approval relatively easily, and you can start to sell your system.





The Players

Now that we've covered how to solve autonomy, let's take a look at all the companies trying to do so, and let's see how well they're doing.





Tesla

Tesla is one of a few companies pursuing autonomy that have chosen not use LIDAR. They realise that the entire road transportation system is designed for vision, that extremely competent vision is a must for developing an FSD system, and that everything LIDAR can do a vision only system can also learn to do. During their Autonomy Investor Day early in 2019, Tesla gave a couple of examples of how a vision only system can also be taught to accurately detect distances.









Andrej Karpathy , Tesla's Artificial Intelligence lead, talks about this from 2:16:47 to 2:22:20. He explains that just like humans are able to perceive depth from vision, computers can also learn to do so through stereo-vision, optical cues from the environment, and data annotations from Tesla's forward facing radar.





Talking about radar, Tesla's full sensor suite consists of eight cameras placed around the vehicle, a forward facing radar, and twelve ultrasonic sensors all around the vehicle. Tesla believes that it can pretty much solve autonomy with vision alone. The forward facing radar is to help detect the distances of objects in the direction in which the vehicle is travelling the fastest, and the near-field ultrasonic sensors help detect objects in very close proximity, such as a pedestrian walking by while the vehicle is backing out of a parking spot.





When it comes to hardware, Tesla has a clear measurable huge advantage over everybody else. Elon had the foresight to realise that hardware is an important piece of the puzzle back in 2016, so he poached a team of the world's best chip architects from AMD. The first chip they built exclusively for Tesla is years ahead of any other chip on the market. Nvidia unveiled a chip that is similar in capabilities to Tesla's just two weeks ago, but it is not going to be available until 2022. By that time Tesla's next generation chip will be out, which is likely going to be on a whole other level.





Far and away Tesla's biggest strength in the race to develop the first FSD system is their data, and how it allows them to iterate better than anybody else, and how it will allow them to gain regulatory approval. To help explain this to you, I'm going to quote my first Investment Thesis from six months ago:

So why do I think Tesla is going to dominate the future robotaxi market? They have a number of advantages, some of which appear to be very hard to overcome for competitors.

Data. When it comes to AI and training neural networks, data is king. In terms of self-driving, the more data you have, the better your neural net will be at correctly identifying objects. The more data you have, the better your software will be at detecting other vehicle cut-ins. The more data you have, the more crazy edge cases you will be able to teach your AI about. Basically, the more data, the better.



When it comes to data, Tesla isn't just ahead, they're not even just dominating, they're so far ahead that it seems like nobody else is even trying. Most competitors have about a hundred (or maybe in the case of Waymo a few hundred) cars on the road with expensive LIDAR equipment to test FSD software and gather data. Mobileye has their cameras in a few million cars, but these are cars sold by its customers such as BMW, so I don't believe they have any way to collect this data, and it's only from one camera, which is not enough information to support FSD. Nissan's ProPilot is used by about 350,000 cars, but like Mobileye this is only a single camera plus radar, and I am unsure if Nissan has any way to gather this data. Furthermore, Nissan only installed the camera on the cars of customers who paid for the feature. This shows a clear lack of understanding of the importance of data, and autonomy as a whole.



Tesla, understanding the utmost importance of data when it comes to achieving full autonomy, has installed their entire sensor suite on every single car sold to customers since October of 2016, regardless of whether the customer paid for Autopilot or not. As a result, Tesla is currently gathering data from 500,000 customer cars every single day. In a year from now this will be approximately 1,000,000 cars, and in a few years they will be gathering data from millions of cars. Basically, their advantage in terms of data is only going to increase from here... exponentially.



Data is not just paramount for creating a FSD car. Remember that I mentioned that part of launching a robotaxi service consists of convincing regulators that one's cars are safer than a human? The average miles per accident in the US is 165,000 miles







Tesla's fleet, on the other hand, drove 20 million miles per day in September of 2018. They had approximately 400-450k cars on the road back then, including some cars without the full sensor suite, but today Tesla has about 500k cars on the road with the full sensor suite, so they are gathering more than 20M miles of data every single day. Tesla is gathering more data in a single day, than all its competitors combined in an entire year, and this difference is only getting bigger.



If another car manufacturer is smart enough, it could replicate this model, but the others simply don't stand a chance. None of the non-car manufacturer competitors can afford to buy this many cars to put on the roads and gather data. Neither can Tesla, but Tesla's customers are paying Tesla to build the cars they need to gather all the data. Simply genius. If I could only name one reason why I'm bullish on Tesla, this would be it right here. When it comes to AI and training neural networks, data is king. In terms of self-driving, the more data you have, the better your neural net will be at correctly identifying objects. The more data you have, the better your software will be at detecting other vehicle cut-ins. The more data you have, the more crazy edge cases you will be able to teach your AI about. Basically, the more data, the better.When it comes to data, Tesla isn't just ahead, they're not even just dominating, they're so far ahead that it seems like nobody else is even trying. Most competitors have about a hundred (or maybe in the case of Waymo a few hundred) cars on the road with expensive LIDAR equipment to test FSD software and gather data. Mobileye has their cameras in a few million cars, but these are cars sold by its customers such as BMW, so I don't believe they have any way to collect this data, and it's only from one camera, which is not enough information to support FSD. Nissan's ProPilot is used by about 350,000 cars, but like Mobileye this is only a single camera plus radar, and I am unsure if Nissan has any way to gather this data. Furthermore, Nissan only installed the camera on the cars of customers who paid for the feature. This shows a clear lack of understanding of the importance of data, and autonomy as a whole.Tesla, understanding the utmost importance of data when it comes to achieving full autonomy, has installed their entire sensor suite on every single car sold to customers since October of 2016, regardless of whether the customer paid for Autopilot or not. As a result, Tesla is currently gathering data from 500,000 customer cars every single day. In a year from now this will be approximately 1,000,000 cars, and in a few years they will be gathering data from millions of cars. Basically, their advantage in terms of data is only going to increase from here... exponentially.Data is not just paramount for creating a FSD car. Remember that I mentioned that part of launching a robotaxi service consists of convincing regulators that one's cars are safer than a human? The, and Tesla's lead competitor Waymo is driving the second most autonomous miles. If they developed a car five times as safe as a human today, went to regulators to request regulatory approval a year from now, and showed them they only had three accidents over (let's be generous and say) 3,000,000 miles. Regulators would probably laugh in their face and say they could've gotten lucky.Tesla's fleet, on the other hand, drove 20 million miles per day in September of 2018. They had approximately 400-450k cars on the road back then, including some cars without the full sensor suite, but today Tesla has about 500k cars on the road with the full sensor suite, so they are gathering more than 20M miles of data every single day. Test Drivers. But data gathering isn't the only thing that Tesla's customers are helping Tesla with in terms of autonomy. For safety reasons Tesla's competitors need to employ people to sit behind the wheel of the cars in their test fleet. Tesla's customers are literally paying Tesla to do this job.



Part of how Tesla makes its FSD software better and better, is looking at ''Autopilot interventions". Whenever a customer is driving a Tesla in autopilot mode and notices an unsafe situation, they will take over control of the vehicle to make sure an accident doesn't happen. Every time this happens, the car sends a notification to Tesla's Autopilot Headquarters along with all the data surrounding the event. Tesla's Autopilot team then looks at what happened, and can improve the software so that it doesn't happen again. If a car manufacturer is smart, they could do the same thing, but there is no way any of the other competitors can hire half a million test drivers, whereas Tesla's customers are paying Tesla to be their test drivers. Again, simply genius.

Basically, Tesla is gathering more data (aka doing more real world testing and encountering more real world edge cases) in a single day than all its competitors in an entire year. Tesla's strategy of turning its products into its test vehicles and turning its customers into its test drivers is pretty much unbeatable. Nobody else is able to test their system on such a scale in so many different driving conditions, nobody is able to gather as much real world data to help with iteration, and nobody else has a realistic plan to gain regulatory approval except for Tesla.





Intel Mobileye

Mobileye which was acquired by Intel a few years ago, also held an Autonomy Investor Day last month (thanks to a commenter on my last blog for pointing this out).









To understand how Mobileye plans to solve the perception problem, watch from 56:10 to 1:00:20 of this video. Mobileye's CEO explains how he sees no way to create a perception system as safe as a human with current technology, so their plan is to create two separate systems to complement each other. He explains that if two systems make a mistake once every ~3,000 hours, two systems running independently will only both make a mistake once every 3,000 * 3,000 = 9,000,000 hours, and as such will be able to rival human levels of safety. Although I have no idea how they will be able to decide what to do when one system identifies a bear on the road, and the other system doesn't see anything. How do they decide which one is right? Perhaps the system would shut down and act extremely carefully until the uncertainty is resolved, but it still doesn't seem ideal.





The bigger issue that I see with this system is that Mobileye seems to assume that if they train two independent systems that make a mistake once every 3,000 hours, that there is no overlap between the mistakes the systems will make. By the way, the two systems Mobileye is creating are a vision based system like Tesla's, and a separate LIDAR system. I am highly skeptical though of their approach, and it seems extremely likely to me that the edge cases that trip up one system, will also trip up the other system.





Mobileye has a number of their own test vehicles for iteration purposes, but likely in the hundreds at best. They may be backed by Intel, but they do not have the resources to own and operate a fleet of hundreds of thousands of test vehicles like Tesla, and they are not a car manufacturer so they cannot copy Tesla's model either.





If you listen to Mobileye's presentation from 1:01:00 to 1:07:00 and listen to the CEO talk about their REM mapping initiative, you might think that Mobileye has access to similar amounts of data to train their system as Tesla. Mobileye's ADAS systems are sold to and installed in millions of vehicles of OEMs, and Mobileye uses data from the cameras in these systems to build maps. They then use these maps for the development of their AVs (and a few other business opportunities) to help with localization among other things.



High Definition Maps & Localization

Here is a short sub-section on HD Maps and localization. HD Maps to me seem like an extremely stupid and illogical idea for an FSD system.



I could be wrong about the assumption I'm about to make, and if there is a self-driving car engineer reading this who could explain it in more detail in a comment, that'd be amazing, but it seems to me that HD Maps are a substitute to depth perception through vision. An FSD system that is already able to perceive distances of objects through computer vision, should already know the vehicle's distance to lane lines, stop lines, etc, and should be able to determine its location within the environment. It should know which lane it is in, it should know how well centered it is in the lane, and it should know how far it is away from a stop line. On the other hand, an FSD system that is unable to detect distances through vision and is relying on LIDAR for depth perception, is unable to figure out where the vehicle is located, because LIDAR can only perceive distances to 3D objects such as vehicles, pedestrians, etc, not to lane lines and stop lines. LIDAR only sees the surface that is the street.



Again, there is a chance I'm missing something here, but it really seems like HD Maps are a very illogical and unwieldy solution to the localization problem. Humans determine their position on the road through vision, and we don't have terabytes of data worth of HD Maps stored in our heads to navigate the roads with. Similar to LIDAR it kind of seems like a shortcut taken by lazy students who don't want to solve vision. At least in the case of Mobileye they have a pretty impressive system to make their maps, thanks to the millions of cars their ADAS systems are installed in.



Here is a short sub-section on HD Maps and localization. HD Maps to me seem like an extremely stupid and illogical idea for an FSD system. This article is the best explanation I've been able to find of what HD Maps are used for, and in essence they are used for localization aka where the vehicle is in the environment. FSD systems with HD Maps can do this by comparing the lane lines, stop lines, and other objects that it sees through perception to the ones from the map.I could be wrong about the assumption I'm about to make, and if there is a self-driving car engineer reading this who could explain it in more detail in a comment, that'd be amazing, but it seems to me that HD Maps are a substitute to depth perception through vision. An FSD system that is already able to perceive distances of objects through computer vision, should already know the vehicle's distance to lane lines, stop lines, etc, and should be able to determine its location within the environment. It should know which lane it is in, it should know how well centered it is in the lane, and it should know how far it is away from a stop line. On the other hand, an FSD system that is unable to detect distances through vision and is relying on LIDAR for depth perception, is unable to figure out where the vehicle is located, because LIDAR can only perceive distances to 3D objects such as vehicles, pedestrians, etc, not to lane lines and stop lines. LIDAR only sees the surface that is the street.Again, there is a chance I'm missing something here, but it really seems like HD Maps are a very illogical and unwieldy solution to the localization problem. Humans determine their position on the road through vision, and we don't have terabytes of data worth of HD Maps stored in our heads to navigate the roads with. Similar to LIDAR it kind of seems like a shortcut taken by lazy students who don't want to solve vision. At least in the case of Mobileye they have a pretty impressive system to make their maps, thanks to the millions of cars their ADAS systems are installed in.

Back to Mobileye

Anyway, Mobileye's data gathering is very different from Tesla's data gathering for a couple of reasons:

Mobileye does not have the ability to test their FSD systems in the vehicles of its customers' customers, and they cannot get feedback about mistakes from disengagements like Tesla.

The data that Mobileye is gathering is 10kb per kilometer, which is very little data. They are gathering a tiny amount of data to help them make maps, but they are not able to use any of this data to train their computer vision system.

Even if Mobileye wanted to, they could not train their vision systems with this fleet, because OEMs probably don't want their customers' cars uploading massive amounts of data to the cloud in the form of videos and images.

The vehicles in this fleet are not capable of receiving OTA updates to my knowledge, and even if some of them can, they're vehicles from dozens of different OEMs. There is no way Mobileye could update the software in these vehicles to tell them to send additional data. The software these vehicles came with only uploads the 10kb per kilometer of map data. So in summary, Mobileye is only able to test their FSD system in a dozen or so cars that they own and operate. And most of the real world data they have access to is also from this tiny fleet.

Mobileye has some other initiatives and is developing LIDAR in-house just like Waymo. An analyst asked about this during their investor day (from 1:57:55 to 1:59:42), and Mobileye's CEO said that they believe that there's an 80% chance they don't need to do it, and could simply rely on other companies to supply them with LIDAR at a low enough price point. However, because in their eyes it is a crucial piece of the puzzle, they don't want to risk it and are developing the sensors in-house as well. If it turns out they don't need it, they will sell this sensor business at some point down the line.

Mobileye produces their own hardware chips, and can leverage a lot of Intel's expertise in that regard. Some time in 2020 they will start deploying a system with 9x EQ5 chips (1:16:00 in video), three of which are for redundancy purposes. These six chips taken together would produce a total of 144 TOPs which will be the exact same as Tesla's chip. However, Tesla's system has full redundancy, whereas I'm not sure if having 6 main chips and 3 backup chips provides the same level of safety. It also appears that Tesla's chip is more power efficient, because it requires 72 watts (1:31:50 in Tesla Autonomy Presentation) compared to 10 watt per chip for Mobileye for a total of 90 watts. All in all though, Mobileye only seems 1-2 years behind Tesla with their hardware chip, and I would not be surprised if they can leverage Intel's technology to keep up with Tesla.

Lastly, in terms of regulatory approval there is no way that Mobileye could gather enough data to convince regulators to allow their system to be used on public roads without supervision. They're barely driving millions of miles in the real world, they need to be driving tens of billions. They're off by a factor of 10,000x.

It seems like that might be the reason as to why Mobileye is taking a very different approach to government regulation (1:10:05 to 1:14:00). They are trying to establish what they call "RSS". RSS is supposed to be a reasonable standard of behavior that is acceptable on the road (e.g. cars are only allowed to break this hard), and as long as all actors in traffic behave by these rules, Mobileye will guarantee that their system never makes a mistake. For example, if a car in front of Mobileye's AV breaks very hard but within the limits, Mobileye will guarantee their AV stops in time because it'll have stuck to a safe following distance, but if the car in front of it literally comes to a dead stop that is outside of what is specified as accepted in RSS, then Mobileye's AV will not be blamed if it crashes. I haven't read through Mobileye's RSS, but I imagine it also stipulates things such as how fast cars are allowed to sway from one lane to another etc.

Mobileye seems to believe that as long as it solves Perception and has an accurate representation of the world around the vehicles, it can create a perfect Planning system that never makes a mistake as long as all actors act within the rules laid out in their RSS. They're trying to talk to regulators about this RSS, and they seem to expect to get regulatory approval this way. To me it quite frankly seems like they just don't know how else they'll ever be able to gain regulatory approval, and I doubt that this is going to be an easy sell to regulators. Seems like a Hail Mary to me.





Waymo





Others using LIDAR





I'll say about all other companies using LIDAR that they are very unlikely to catch up to Waymo/Mobileye. LIDAR makes it possible to make a relatively well working demo in a short amount of time, and startups can leverage this to raise funding and show progress to investors, but Waymo is too far ahead in terms of vertical integration and developing its own cheap sensors, and nobody is able to create HD Maps on the scale that Mobileye is able to through the ADAS it sells to customers.



Even GM's Cruise,



From 1:26:04 to 1:26:44

The number of companies working on self-driving vehicles is enormous. This picture doesn't even do it justice, because half of these companies are just car manufacturers who have a tiny FSD research unit, and there are dozens of other smaller startups also working on the problem. Therefore I cannot cover all of them in detail, nor do I want to.I'll say about all other companies using LIDAR that they are very unlikely to catch up to Waymo/Mobileye. LIDAR makes it possible to make a relatively well working demo in a short amount of time, and startups can leverage this to raise funding and show progress to investors, but Waymo is too far ahead in terms of vertical integration and developing its own cheap sensors, and nobody is able to create HD Maps on the scale that Mobileye is able to through the ADAS it sells to customers.Even GM's Cruise, which is valued at about $20B , is very unlikely to leapfrog Waymo according to George Hotz.

Comma.ai





Comma.ai wanted to sell both the retrofit hardware package and the software, but the company had to abandon this plan because



So for Perception they're using a vision based system. For Iteration



But the biggest issues I see with Comma.ai are as follows:

Their hardware chip is insufficient to reach full autonomy. I can't imagine their $600 hardware retrofit package's hardware chip is anywhere close to being powerful enough to support full autonomy. Their sensor suite is great for driver assistance features, but seems insufficient for full autonomy. It contains two windshield mounted GoPros, and I believe also leverages dashcams and radar data of cars that are equipped with them, but all of this seems insufficient. An indication to this fact is that their lane changing feature can change lanes, but cannot determine when it is safe to change lanes and requires the driver to determine this. As ADAS from companies like Mobileye become better and are included in more vehicles, the market for Comma.ai's hardware retrofit will shrink. If the open source software that runs on their hardware is not better than other ADAS, nobody will buy their hardware anymore, and they can not expand their real world testing and data gathering, that they need to improve the open source software and reach full autonomy. The company itself seems to be a little shaky. George was forced to step down as CEO, although he is still the president and lead enginner. The company also recently moved all the way from Silicon Valley to San Diego, which I don't think is a great sign either.

What would you get if somebody was trying to solve autonomy by copying Tesla, but started from scratch as a startup? The answer is Comma.ai. They do not use LIDAR and rely exclusively on vision, and in some cases some radar data. However, they are not able to install their system into the vehicles they sell to their customers like Tesla, because they are not a car company, so they've come up with a system that can be retrofitted to many (but not all) existing cars. Customers buy this system, which consists of a simple computer and a camera, from Comma.ai and install it into their own vehicle. They then use Comma.ai's Openpilot software which is similar to Autopilot and provides cruise control and lane keeping assist features.Comma.ai wanted to sell both the retrofit hardware package and the software, but the company had to abandon this plan because the NHTSA demanded they comply with too many safety regulations , so they decided it would be easier to sell the hardware only and open source the software. George Hotz, the founder of Comma.ai, has said that they aim to be the Android of autonomy.So for Perception they're using a vision based system. For Iteration they're using real world testing and data from 4,500 customer vehicles , which have so far traveled over 10 million miles. These are very good numbers compared to most of the industry, but multiple orders of magnitude away from Tesla's, and Tesla is selling way more vehicles than Comma.ai is selling retrofit hardware. To ever gain regulatory approval, assuming they can get to human safety levels, would require them to convince many more customers to buy their retrofit hardware for cruise control and lane keeping assist features.But the biggest issues I see with Comma.ai are as follows:

Pronto.ai & others not using LIDAR





Pronto.ai also offers a retrofit hardware package, but instead of passenger vehicles they are targeting trucks, and their package is much more expensive at $4,999. They say this is a



Other than that, they are extremely similar to Comma.ai in their approach of offering driver assistance features (but not open source), and in their plan to slowly improve this to a fully autonomous system that's safer than a human. Even the name of their software, Copilot, is almost the same as Tesla's Autopilot and Comma.ai's Openpilot.



Other than that not much is known. We don't know how many trucks are part of their real world testing and data gathering fleet. We can assume that for $4,999 there is some sort of hardware chip included, but they are likely reliant on a supplier like Mobileye or Nvidia. All in all, their strategy seems good (a copy of Tesla), but questions remain about their progress and how big their fleet can get.



Putting it all together There is at least one other company that has chosen not to use LIDAR, and I assume there are more. Pronto.ai's founder, Anthony Levandowski, used to be very vocal about his support for LIDAR when he worked as an engineer at Waymo. He went so far as to say "we have got to start calling Elon on his shit" in response to Elon's public stance against LIDAR, but Anthony has since then founded Pronto.ai, a startup that is very similar to Comma.ai in the way it is copying Tesla's strategy.Pronto.ai also offers a retrofit hardware package, but instead of passenger vehicles they are targeting trucks, and their package is much more expensive at $4,999. They say this is a "deeply discounted price for a limited set of customers" . It includes an unspecified number of cameras, and a radar.Other than that, they are extremely similar to Comma.ai in their approach of offering driver assistance features (but not open source), and in their plan to slowly improve this to a fully autonomous system that's safer than a human. Even the name of their software, Copilot, is almost the same as Tesla's Autopilot and Comma.ai's Openpilot.Other than that not much is known. We don't know how many trucks are part of their real world testing and data gathering fleet. We can assume that for $4,999 there is some sort of hardware chip included, but they are likely reliant on a supplier like Mobileye or Nvidia. All in all, their strategy seems good (a copy of Tesla), but questions remain about their progress and how big their fleet can get.

If you're still with me, let's try to piece all of this autonomy information together, and let's see what Tesla's chances are in this industry. To do so, let's look at how well Tesla is positioned in each of the things important to creating an FSD system.

Perception. Vision is the most important element here. You can take a shortcut by using LIDAR, but you're going to need a world class computer vision system eventually to get to full autonomy. HD Maps in theory is a good alternative to vision for localization, but in practice it is extremely hard to make it work because of the scale required to make it work worldwide, and because they need to be kept up-to-date. Only Mobileye's mapping solution seems like it could work, but why not just do localization through vision. Tesla is one of a few companies entirely focused on solving vision, and among those companies Tesla is the clear leader.

Vision is the most important element here. You can take a shortcut by using LIDAR, but you're going to need a world class computer vision system eventually to get to full autonomy. HD Maps in theory is a good alternative to vision for localization, but in practice it is extremely hard to make it work because of the scale required to make it work worldwide, and because they need to be kept up-to-date. Only Mobileye's mapping solution seems like it could work, but why not just do localization through vision. Tesla is one of a few companies entirely focused on solving vision, and among those companies Tesla is the clear leader. Planning. I haven't talked much about approaches that companies have to planning, because there isn't a lot of information available. Tesla showed a number of impressive examples during their Autonomy Investor Day of how they use things like shadow mode to test and refine new features, but I have not been able to find anything about how others' approaches may differ.

I haven't talked much about approaches that companies have to planning, because there isn't a lot of information available. Tesla showed a number of impressive examples during their Autonomy Investor Day of how they use things like shadow mode to test and refine new features, but I have not been able to find anything about how others' approaches may differ. Iteration. Real world data is everything here, and nobody has a fleet that can be used for real world testing and data gathering anywhere close to the size of Tesla's. As a matter of fact, all of Tesla's competitors combined don't even have a fleet that's in the same ballpark as Tesla's. Furthermore, Tesla's production (and therefore Tesla's fleet size) is increasing exponentially, so this is pretty much Game, Set, and Match. And I haven't even talked about things such as Operation Vacation, that once completed will supposedly automate Tesla's entire training & iteration process, so that Tesla's Autopilot team can go on a vacation, hence the name.

Real world data is everything here, and nobody has a fleet that can be used for real world testing and data gathering anywhere close to the size of Tesla's. As a matter of fact, all of Tesla's competitors combined don't even have a fleet that's in the same ballpark as Tesla's. Furthermore, Tesla's production (and therefore Tesla's fleet size) is increasing exponentially, so this is pretty much Game, Set, and Match. And I haven't even talked about things such as Operation Vacation, that once completed will supposedly automate Tesla's entire training & iteration process, so that Tesla's Autopilot team can go on a vacation, hence the name. Hardware. As I've talked about, Tesla's autonomy chip is at least one to two years ahead of the competition's chips from Mobileye and Nvidia.

As I've talked about, Tesla's autonomy chip is at least one to two years ahead of the competition's chips from Mobileye and Nvidia. Regulatory Approval. Nobody, and I mean absolutely nobody, has a clear path to proving their system saves lives, except for Tesla. Mobileye's RSS seems like a moonshot that's unlikely to convince regulators. Tesla is the only player that will be able to gather enough data to prove beyond any reasonable doubt that their system saves lives. Taking this all in, I think the question we have to ask ourselves is not who's going to be the first to solve autonomy, that will be Tesla for sure. The question we need to ask ourselves is whether anybody else will be able to solve autonomy before the point of no return beyond which Tesla's monopoly is inevitable. I think the answer to that second question is quite possibly "no".





Waymo does not stand a chance. Their strategy is flawed in that they rely on LIDAR and an effectively impossible to scale HD Maps system. Their fleet is also too small. Other companies using LIDAR are in even worse shape than Waymo. All the same flaws, but years behind.





The only company using LIDAR that stands a chance is Mobileye. It may be better to use vision for localization, but their HD Maps solution works at a large scale. Moreover, they are developing a vision only system in tandem to their LIDAR system. Publicly they say this is to build two complimentary systems that work together, but it could be that internally they feel like they should hedge against LIDAR being a dead-end, or that they already see the writing on the wall and have already shifted most of their focus to the vision only system. Mobileye also makes their own hardware chip that's about as close to Tesla's as can be (one to two years behind), but the crucial ingredient that Mobileye is missing is large scale real world testing and data gathering. They do not have a fleet like Tesla's, and without that regulatory approval could be a problem, and most worryingly it will take them a very long time to build a vision system powerful enough to support full autonomy. In the words of their own CEO: "I don't believe there's technology that can meet those propabilities of failure. Why do I think this? I know what the probabilities are in ADAS, and we are the leading company in providing these.".





So that leaves the companies not pursuing LIDAR. Can any of them hope to compete with Tesla? Comma.ai to be frank does not seem to have their stuff together, so I don't think they will. Pronto.ai? Maybe, but are they really going to find enough trucking companies willing to pay their 'deeply discounted price' of $4,999 per truck to build a meaningful fleet? That seems unlikely.





Therefore, in my eyes it appears very possible that Tesla will monopolize autonomy.





What could go wrong?

During their investor day Mobileye's CEO said that they believe there will be between two to four companies that dominate autonomy. If he is right and there will be a company that is able to compete with Tesla in autonomy, my money is on Mobileye. There could be advantages of HD Maps that I'm simply not aware of, and in that case Mobileye is suddenly in a much better position, because they're the clear leader in HD Mapping technology.





Another possibility is that Mobileye comes to realise the error of its ways, and decides it needs to copy Tesla's strategy ASAP. The main thing it lacks is a large fleet for large scale real world testing and data gathering. If it can somehow make deals with car manufacturers to roll out versions of its FSD software in customer vehicles, to use these vehicles for testing, to enable OTA updates, and to gather data from the vehicles to improve the Mobileye FSD system, then it'd suddenly be in a good position to compete with Tesla.





It seems unlikely that this will happen at all though, especially at a large scale in a reasonable time span. It probably takes months, if not years, to sell ADAS systems to car companies and negotiate the details of those contracts. Even if they pull all this off, Tesla would still have a large head start and infrastructural advantages like Project Dojo and Operation Vacation.





Autonomy Summary

Are you still with me? A few weeks ago I was thinking of writing an entire blog just about autonomy, and looking at how long this section has become on its own, that might've been a good idea, because this blog is starting to become a monster in size. Either way, I hope you now have a better understanding of what it takes to solve autonomy, and why Tesla is so far ahead. Their strategy is pretty much unbeatable, and in my opinion it's looking like Tesla will at the very least be the market leader in autonomy, and has very strong potential to monopolize this industry.





One final point I want to make is that Tesla's automotive and autonomy businesses are closely related. If Tesla solves autonomy tomorrow, years ahead of everyone else, they would not have to worry about demand for their vehicles for any of those years, because their vehicles would suddenly be FAR superior to anybody else's.





Similarly, Tesla's monopoly potential in its autonomy business is reliant on a strong execution in its automotive business, in particular in terms of manufacturing. If Tesla solves autonomy and continues to only sell it through its own vehicles (most likely), and if then Tesla is slow to ramp manufacturing, it might leave some room for a competitor to grab some market share. Because if Tesla's FSD system (hardware + software) is only available in Tesla vehicles, but Tesla's production is lacking, it gives competitors more time to solve autonomy and offer it for sale in non-Tesla vehicles.





AMaaS

AMaaS stands for Autonomous Mobility as a Service. It basically means autonomous transportation on demand, either for passengers who need a ride from point A to point B, or for cargo. Today, MaaS (non-autonomous AMaaS) exists in the form of ride-hailing services like Uber, Lyft, and Grabtaxi, regular taxi services, and logistics companies such as DHL. All of the transportation over roads that these services offer requires drivers, but when autonomy is solved this will slowly but surely transition towards driverless services that are able to offer the same product at a fraction of the cost. Basically, this industry is going to be disrupted BIG time, and will be unrecognizable five to ten years after autonomy is solved. Let's start off this section by looking at what will be required to operate a successful AMaaS business.





The Ingredients to Successful AMaaS

There are business models in which the AMaaS provider will not have to provide every ingredient, but all of the following are required in the overall system.

Platform. This is the only ingredient that an AMaaS provider absolutely must do themselves. There has to be an interface (most likely a mobile application) ala Uber/Lyft through which passengers can order rides. To request the transportation of cargo will also require an interface or application. Vehicles. These are needed to transport the passengers/cargo. FSD System. To control the vehicles. Servicing. Vehicles will have to be serviced in the case of defects or damage. Charging. Electrification of all road vehicles is only a matter of time, so charging will be necessary to provide an AMaaS service in the future. Insurance. Even when FSD systems reach human levels of safety, they will still be far from perfect and continue to make mistakes for a while to come. If the FSD system doesn't come with insurance included, somebody else will have to take care of this. Infotainment. Not a must have, but an enormous differentiator for passenger AMaaS. If passengers have multiple choices of service providers, this could determine the winner. Mobileye summed up the requirements for a robotaxi service slightly differently during their investor day:

https://www.youtube.com/watch?v=9JWvzuOlAKs&t=3517s

L5 is Infotainment and the Platform, L4 is also the Platform, L3 is Servicing/Charging/Insurance, L2 is Vehicles, and L1 is FSD System.

You might've noticed that Tesla does every single one of these. Tesla is even already working on a platform, but more on Tesla in a bit. Let's first look at some of the other players.





The Players

The Incumbents

There are a number of MaaS providers that might be looking to transition to AMaaS when autonomy arrives. Actually, all of the smart ones will be looking to do so, because their current business will not survive the transition from MaaS to AMaaS. A driven vehicle is simply not cost competitive with a driverless vehicle.





The incumbents in the MaaS industry are taxi companies, ride-hailing companies (Uber, Lyft, etc.), logistics companies (DHL, FedEx, trucking companies, etc.), car rental companies, and also rail and bus companies if you want to include all (under-)ground transportation, but for simplicity's sake let's leave out bus and rail.





These companies can theoretically transition to an AMaaS business model. Uber's revenue and profits (if it had any) would diminish if it started allowing AVs on its platform, but its business would be intact. Similarly, logistics companies would have to get rid of a lot of employees, but it could replace them by AVs and be competitive in the AMaaS future.





All of this relies on one important thing however, the commoditization of AVs, and herein lies the biggest danger for the incumbents. It doesn't look like the companies leading in the development of autonomy are going to allow this to happen. The license of its FSD software that Tesla is selling only allows for private use and use on their own AMaaS platform. Mobileye plans to launch its own robotaxi service in a few years, but doesn't plan to sell its FSD system to consumers until much later when prices have further decreased. This suggests that those licenses will also limit use to private use, because a vehicle with an FSD system could generate hundreds of thousands of $s operating as a robotaxi, so the cost of their LIDAR equipment should not be prohibitive to selling the system to consumers. Waymo (if they are able to solve autonomy) also appears to have its eyes set on launching its own AMaaS service, so I doubt they'd let their technology be used by competitors either.





Some companies like Lyft and Uber are developing AVs in-house, but their autonomy programs are pretty much a joke. Uber ranked dead last in disengagement statistics in California in 2018 . The survival of the incumbents is 100% dependent on the commoditization of AVs, which is not looking like it'll happen.





The Automotive Industry

There are a number of car companies with mobility initiatives. They seem to realise that this is going to play a major role in the future of transportation, and they've seen the success (for now) of companies like Uber and Lyft, so they want to get in on it. However, they face the exact same issue as the MaaS incumbents in that they are 100% dependent on the commoditization of AVs.





The only tiny slimmer of hope they have of playing a role in the AMaaS industry is through a partnership with a company that solves autonomy. Tesla obviously doesn't need a car company as a partner, but companies like Mobileye and Waymo can develop all the FSD systems they want, but without actual vehicles they cannot be an AMaaS provider.





This seems difficult though, because vehicles are very commoditized, but FSD systems very much are not. Therefore the autonomy companies have all the power in the relationship. It'll be like a bar on a Friday night with one or two hot girls and a dozen guys.





Autonomy Companies

Comma.ai who only plans to sell retrofit hardware kits is the exception among the autonomy companies. Many of them have not announced clear go-to-market plans, but the leaders Mobileye and Waymo plan to leverage their autonomous technology to launch AMaaS services. To do so, Mobileye for example plans to partner with two other companies









The exact details of the joint venture have not been announced yet, but I'd expect that for the most part Mobileye will simply buy vehicles from Volkswagen and pay Champion Motors, an Israeli car dealer , for servicing and insurance. After launching in Israel in 2022, Mobileye plans to expand to Europe not long thereafter, and eventually other markets as well.





Waymo's plan is almost the exact same, but their rollout will be more limited due to one of the major flaws in Waymo's autonomy strategy, HD Maps.





Mobileye's and Waymo's plans will take a lot of time, and more importantly unimaginable amounts of capital to scale. Perhaps if they are able to lease vehicles that'd help a lot, but they still have to pay for the sensors and other infrastructure. Once it starts rolling and bringing in revenue though, it could potentially scale faster and faster. Except for Waymo of course, who will still have to make HD Maps of every new area they want to expand to first.





The Tesla Way

Tesla's approach is very different from anybody else's. Mobileye and Waymo's plan is fantastic in that it will allow them to take all of the profits from their AVs, but it can't be scaled fast because of the enormous capital requirements. If AVs become commoditized, an approach like Uber's has the advantage that they don't need much capital to scale, because it's an open platform that anybody can use. In return Uber will have to be satisfied with only part of the profits.





Tesla plans to combine these two approaches to get the best of both worlds. They realise that to scale fast and to capture massive market share as fast as possible, they can't just launch a service with only Tesla owned vehicles. Tesla (or any company for that matter) just doesn't have enough capital, and they also have to use their capital to scale their automotive business.





So as I'm sure many of you are already aware, what Tesla plans to do is to allow their customers to rent out their AVs on Tesla's platform, which they've called the Tesla Network, very similar to how Uber works today. This allows them to scale as fast as they can produce vehicles, which is starting to be pretty fast already and increasing at an exponential rate.





This might seem like Tesla will not be able to capture as much profit as a company that uses Mobileye's or Waymo's strategy. First of all, if one would have to choose between gaining massive market share or all of the profits, one should choose market share. Thirty percent of the profits of a worldwide AMaaS service will be worth more than 100% of the profits of a small scale localized AMaaS service.





But this strategy doesn't actually mean Tesla will have to give up a large amount of the profits. As much as Tesla has spare capital available, they can invest this into vehicles that go into their own fleet of AVs. You might think that the amount of spare capital Tesla has available is going to be a drop in the bucket, but I'll show you later in the Financials section that once Tesla Network starts to get up to speed, Tesla will have massive cash flows available that it can invest into its own fleet of AVs.





Another huge advantage that Tesla will have over everybody else in the AMaaS industry is vertical integration. Tesla has in-house every single ingredient needed to be successful in the AMaaS industry:





Infotainment. I think it's no coincidence that Tesla is investing so heavily into their vehicle infotainment already. It may also help them sell cars today, and it may make them more enjoyable, but I bet the real reason is to be years ahead of the competition in infotainment when AMaaS becomes a reality. Insurance. Once again, it is no coincidence that Tesla has launched Tesla Insurance earlier this year. Yeah, it provides a slight financial benefit to their customers in the form of slightly reduced insurance premiums, but I doubt Tesla is making much, if any, profit from this. The real reason they are doing this is because of Autonomy and AMaaS. Charging. Biggest charging network on the planet? Check. Robocharger? Check. Servicing. Tesla does this in-house, and has been aggressively improving quality and efficiency as we've discussed previously. FSD System. Years and years ahead of the nearest competitor. Vehicles. Cheapest, most efficient, longest lasting EVs ever? Yup, we got 'em. Platform. Hmm, does Tesla have an AMaaS platform? As a matter of fact they've already gone through multiple versions of the Tesla Network, they just haven't launched it to the public yet! (See video below) Watch from 8:01 to 8:54.





In terms of launch and expansion, you might expect Tesla Network to be ready worldwide with the press of a button, just like autonomy could be available worldwide with a single OTA update. I highly doubt this will happen though. I expect Tesla to launch Tesla Network in the USA first. It's likely that Tesla's FSD system will need tweaking in various geographical regions, so it seems likely that autonomy will first be ready in their biggest market, the USA, and that Tesla Network will also first be launched in their biggest market, the USA.





Another reason for this is because there will likely need to be a lot of tweaking in terms of customer experience. Tesla may have already gone through a few versions of their Tesla Network software, but its unlikely to be perfect right out of the gate. So it's very likely that they will launch on a smaller scale first to tweak and improve before expanding. Thereafter though, Tesla's rate of expansion will be very fast. They are not limited by HD Maps like Waymo, and they are also not limited by capital like Waymo and Mobileye thanks to their customer vehicles fleet.





Tesla's Unbeatable Trifecta*

Even if Tesla is not the only company to solve autonomy, even if Tesla's currently superior looking AMaaS strategy is matched by the competition, and even if Tesla's vertical integration turns out to be completely meaningless, we can come back to Tesla's automotive business. Tesla's EVs are the cheapest, most efficient, and crush everybody else's in terms of longevity. Tesla will still dominate AMaaS because they can undercut everybody on price. If your vehicles are more expensive, cost more money to operate, and last half as long, you cannot compete with Tesla.





This trifecta* that Tesla has going on with its automotive business, its autonomy business, and its AMaaS business appears to be truly unbeatable. It's looking like they have the potential to crush all three individually in a vacuum, but the world is not a vacuum. Even if one of the three doesn't work out as well as it's looking like they will, Tesla's domination in the other two will likely make up for it. Even if all three go a lot less well than it's looking like, Tesla should still find a lot of success due to the enormous synergies between these three businesses.





Going back to Tesla's three main competitive advantages. If Tesla holds onto its lead in batteries, if Elon remains CEO long enough, and if Tesla's employees keep kicking ass, I believe Tesla has serious potential to dominate, dare I say monopolize, terranean transportation.





*not sure if I can use the word trifecta like this, but it sounds good





What could go wrong?

Looking at AMaaS there are a few things that could cause Tesla issues. The main thing is that Tesla will have to solve autonomy first. Without autonomy there will be no AMaaS. Smaller issues that I don't expect to be too problematic will be media backlash over accidents. As long as Tesla's FSD system is safer than a human, and they can prove this with statistics, this should not ruin their plans.





Bigger issues will be brought forth by the fact that it's an entirely new business venture for Tesla. Things will not go smooth right from the beginning, and they'll likely need to iterate and improve on their AMaaS service for a while before it becomes a really great experience. The biggest issues however, are likely hard to foresee at this point. AMaaS is not just uncharted territory for Tesla, but uncharted territory for humanity. Tesla is certainly in the best position, but we have to factor in some degree of risk due to unforeseen events.





Looking at terranean transportation as a whole I honestly don't see any non-freak scenarios in which Tesla does not become the market leader at least. They do freaking everything, and they do almost all of it (service is still a work in progress) so damn well. An exciting future awaits courtesy of Tesla and Elon Musk.





Non-Terranean Transportation

Before we head into the final section covering financials, it's time for a brief breather. In Master Plan, Part Deux, Elon said he plans for Tesla to cover all major forms of terrestrial transport. Tesla's automotive business will cover all transportation over roads, and Tesla will work together with TBC to create the Loop and Hyperloop, which if successful would compete with rail and MRTs. But to cover all forms of terrestrial transportation, Tesla would also have to cover non-terranean transport over water, and through the air.





For the people skeptical about Tesla getting into this, right before the Cybertruck unveil Tesla's Instagram page briefly said Cars, Boats, & Airplanes. Take that as you will.





Boats





The history of electric boats is very similar to that of electric cars. When motor boats first came around electricity was actually the main form of propulsion. It was not until the 1920s, when the internal combustion engine became dominant, that it shifted to the gasoline boats that we have today.





There are many different types of boats. Some can be electrified with today's technology, and countries like Norway and China are already pushing hard to electrify the boats that can be electrified. These are boats such as ferries, and container ships that travel very short distances like the Yara Birkeland





Similarly, it should not be difficult to bring to market electric versions of most types of pleasure boats, and in fact some pleasure water transportation devices such as jet skis are already getting electrified





However, pure electric versions of longer distance vessels like the huge containers ships that roam our oceans, are probably not going to arrive any time soon. Battery technology has simply not advanced enough yet for these to be feasible , even with the use of solar panels.





I'd imagine that if the world were to really push for reducing carbon emissions of ships, and if Elon really wanted to do something about it, he could probably electrify inland ships with the help of charging infrastructure built out along popular shipping routes. But for the massive long distance container ships to become electric, we're going to need some huge energy storage breakthroughs.





When Tesla starts getting into the boat industry, a lot of what makes them successful in the automotive industry will also help them in the boat industry. Tesla has a massive lead in batteries, electric powertrains, and electric motors, so at the very minimum they should be able to supply these to boat companies. If boat companies are willing to electrify their offering, I could see Tesla end up simply as a supplier to them. I don't know if Tesla will want to actually manufacture boats in-house, but you never know considering how unafraid they are of vertical integration, and doing things themselves.





I'm sure Tesla could get some smart engineers together in a room, figure out how to build amazing boats based on their EV technology, and successfully compete in the industry. Whether they actually will, and if so when, is anybody's guess.





Airplanes





Although I'm not super confident Tesla will ever do more than be a supplier in the boat industry, I am super confident that Tesla will get into the airplane industry at some point. To explain to you why, I'm going to be lazy and quote myself from six months ago:

energy density of 250 Wh/kg a path to a battery energy density of >500 Wh/kg One of the improvements that Maxwell has made in battery tech is an improvement in battery energy density. Tesla's current batteries appear to have an. Maxwell has demonstrated energy densities of over 300 Wh/kg, and has identified. This means that if all goes according to plan, in the future the same battery will be able to hold more than twice as much energy.

When I first heard about this, I instantly thought of Elon's plans to design an EVTOL (Electric Verticle Take Off and Landing) Aircraft. Elon has talked about having a design for such an aircraft numerous times, and said he would love to build it, but that he believes battery energy density has to improve to over 400-500 Wh/kg to make it work.

It's definitely a long term thing, but I think that if Tesla succeeds in their automotive business, the chance that they announce plans to produce an electric aircraft by 2035 might be as high as 80-90%. After all, their CEO also just happens to be the lead designer at the world's number one rocket company, so he knows a thing or two about aerospace engineering.

Financials





I think overall this Investment Thesis 2.0 has turned out to be more technical than



I'm going to be excluding a few things from the financials, namely Boats, Airplanes, and Public Transportation. The timelines and the details of these potential future business ventures are simply too vague at this point. I could research some numbers about the global airplane industry, make a ton of assumptions, and then create a financial model of it, but it'd end up being mostly speculation, and I don't think it's really worth my or your time getting into that. I've also excluded "Services and other" revenues and profits from this model, because Tesla does not aim to profit from it, so long term it should not impact valuation.



I'm going to cover each business (Energy, Automotive, Autonomy, AMaaS) individually first, and I will put it all together at the end. Unlike last time, I'm not going to present a bear and a bull model. The bear model I presented last time was quite frankly far too conservative, except for maybe a few points (autonomy margins, OPEX (operating expenses), valuation multiple). The single model I'll be presenting here is bullish, but by no means overly so. It assumes Tesla will continue executing similarly to how it has in the past, which I think is a reasonable assumption to make. It's possible that if something happens to Elon, these numbers to turn out to be incorrect. However, based on all the information we have available to us today, I believe this model to be on the aggressive side, but reasonable.



The model I'm presenting projects the next decade and goes up till 2030, but this wouldn't be a "Monopoly Potential" blog without financials that show Tesla's potential. So in addition to these, I have also modeled out a few different 'end-states' of what Tesla's business would look like financially if it ends up with various amounts of market share.



If you're on a computer and would like to follow along more closely with what we're doing, or if you completely disagree with things I say and think you know better, I suggest you open the financial model that I've uploaded to Google Drive below. You'll be able to see how everything works, learn about finance and financial modeling, and you can change things you disagree with me on. There are also a few parts in the model for you to fill in your own predictions, and you should be able to do so in Google Sheets with the Excel version of the model.



Tesla Investment Thesis 2.0 Financial Model - Excel

Tesla Investment Thesis 2.0 Financial Model - Numbers

Tesla Investment Thesis 2.0 Financial Model - PDF



But now without further ado, let's get into the first part, Tesla Energy.



Notes:

Fields in the color Cyan are guesses, assumptions, estimations, etc.

Fields in the color Red indicate funky data. Often due to changes in the way Tesla has reported certain numbers over time.

There are a few fields in the color Green. These are my estimations for Tesla's potential.

There are a few fields in the color Yellow. These are for you to fill in your own estimations about Tesla's potential.

Energy It's now the morning of the 29th of December, and I've been pretty much writing non-stop (12 hours per day) for 6 days straight. I was very worried for most of the week that I wouldn't be able to finish this gargantuan thesis before the end of the year as I had originally set out to, because especially the autonomy section turned into a much bigger ordeal than I had originally anticipated. At this point though, I'm fairly confident that I will finish everything before the end of the year. This financials section is the last section, and afterwards I'll just have to go back over everything to check for errors and polish.I think overall this Investment Thesis 2.0 has turned out to be more technical than the original one , but I'm going to try to make the financials section of this one a little more welcoming to readers who don't 'speak spreadsheet'. I'm going to explain much more about how this all works, and basically give you a crash course in financial modeling.I'm going to be excluding a few things from the financials, namely Boats, Airplanes, and Public Transportation. The timelines and the details of these potential future business ventures are simply too vague at this point. I could research some numbers about the global airplane industry, make a ton of assumptions, and then create a financial model of it, but it'd end up being mostly speculation, and I don't think it's really worth my or your time getting into that. I've also excluded "Services and other" revenues and profits from this model, because Tesla does not aim to profit from it, so long term it should not impact valuation.I'm going to cover each business (Energy, Automotive, Autonomy, AMaaS) individually first, and I will put it all together at the end. Unlike last time, I'm not going to present a bear and a bull model. The bear model I presented last time was quite frankly far too conservative, except for maybe a few points (autonomy margins, OPEX (operating expenses), valuation multiple). The single model I'll be presenting here is bullish, but by no means overly so. It assumes Tesla will continue executing similarly to how it has in the past, which I think is a reasonable assumption to make. It's possible that if something happens to Elon, these numbers to turn out to be incorrect. However, based on all the information we have available to us today, I believe this model to be on the aggressive side, but reasonable.The model I'm presenting projects the next decade and goes up till 2030, but this wouldn't be a "Monopoly Potential" blog without financials that show Tesla's potential. So in addition to these, I have also modeled out a few different 'end-states' of what Tesla's business would look like financially if it ends up with various amounts of market share.If you're on a computer and would like to follow along more closely with what we're doing, or if you completely disagree with things I say and think you know better, I suggest you open the financial model that I've uploaded to Google Drive below. You'll be able to see how everything works, learn about finance and financial modeling, and you can change things you disagree with me on. There are also a few parts in the model for you to fill in your own predictions, and you should be able to do so in Google Sheets with the Excel version of the model.But now without further ado, let's get into the first part, Tesla Energy.

For simplicity's sake, I'm going to assume all of Tesla's solar is sold, and Tesla does not lease any solar. This is of course incorrect, and I do have a model that separates between solar sales and leases, but Tesla only very recently started releasing more detailed information in regards to this, so the model is forced to make a lot of assumptions, and I believe it would just confuse most of you reading this. Modeling all solar as direct sales instead of leases inflates revenues and profits in the short term, but underestimates them in the long term. When you lease something, you pay extra for the financial service that is being provided after all, and Tesla's automotive leasing business is a prime example of how this works.





Energy Storage

Let's start by modeling Tesla's Energy Storage business. To do this we need to figure out the following numbers under "Storage" in the "Energy" tab:









Past Data

MWh Deployed is always reported by Tesla. The numbers for 2016 and onwards can easily be found in Tesla's quarterly financial reports, as well as in Tesla's quarterly and annual SEC filings. The numbers for 2014 and 2015 were calculated off of $ / MWh.

is always reported by Tesla. The numbers for 2016 and onwards can easily be found in Tesla's quarterly financial reports, as well as in Tesla's quarterly and annual SEC filings. The numbers for 2014 and 2015 were calculated