Giving Tesla a 15-Year Head Start Was a Bad Idea
First Mover Advantage, Preferential Attachment & Zipf's Law
Raw data source: Cox Automotive Kelly Blue Book Q3 2022 Electrified Light-Vehicle Sales Report
Being the first mover in a market has advantages and disadvantages, but mostly advantages, including economies of scale, brand positioning, patents, and supply chain relationships. Another underappreciated advantage is gaining knowledge and information in the early stage before others have gotten started, which can be leveraged in later stages of the game to accumulate yet more knowledge and information in a virtuous cycle.
Wright’s Law is an empirical observation that the cost of producing most technologies tends to exponentially decay as a function of cumulative production volume. In other words, every time total all-time production doubles, the cost improves by a constant percentage. (Want to learn more about Wright’s Law? Here’s an intro from Ark Invest and Wikipedia.)
The improvement percentage per doubling may vary from one company to another, because it reflects the rate of innovation and how fast each company is learning by doing.
The rate of production growth also will vary from one company to another. The faster a company scales, the faster they learn and improve, all else being equal.
Innovation tends to enable faster scaling while also reducing cost, especially in manufacturing when the engineers can devise ways to eliminate parts and processes that aren’t necessary. Reducing work and floor space enables more production per day with existing facilities and workforce while also saving cost. This in turn enables faster innovation, more money to invest in expansion, and thus feeds into a virtuous cycle of growth.
This is why pace of innovation is all that matters in the long run for any market in which there is still a significant cost delta between the best producers and their less successful competitors. I think for the car market, this first mover advantage is strong for key areas including engineering, supply chain, design, manufacturing, and servicing. Tesla’s head start is a gigantic and probably insurmountable barrier to competition in many ways. Today I want to focus on how it’s given them data and experience for engineering.
Apple and Foxconn, for instance, are formidable companies but they haven’t even started an attempt at EV mass production. New companies like Rivian and Lucid have been working longer but are much less capitalized and less known than Apple & Foxconn, and they still haven’t actually been shipping cars to customers for years. Apple coming to the car market would be like when Michael Jordan left basketball to play baseball in 1993. Despite being a world-class elite athlete at the peak of his athletic prime who had just led his team to three consecutive NBA championship wins, he did not even make it to the Major Leagues as a baseball player. The obvious reason was that he had not practiced baseball since high school.
When engineers design a machine like a car, they have simulation models and physical test data showing that stuff should work in theory, but there's still significant uncertainty that can be resolved only by actually building and shipping product and seeing what reality has to say. Likewise, the manufacturing system—The Machine That Makes the Machine—has a design with models and test data and all the associated uncertainty. Automotive engineering has even more uncertainty than most machine designs because the manufacturing is complex, customer expectations are high, and the duty cycle will expose the vehicle to heavy stresses such as vibration, extreme temperatures, foreign object debris, salt, moisture, and even ultraviolet radiation. Plus, the expected service lifetime is more than a decade. Accelerated life testing is crucial for planning this and understanding the effects of these various degradation mechanisms, but you just never really know until actually putting the cars in service and waiting for them to get abused and get older.
With greater design uncertainty, engineers need to apply bigger safety margins and sometimes need to add extra layers of redundancy in case of failure. All of this comes at a price: reduced vehicle performance on key design criteria like cost, range, acceleration, handling, safety, etc. Uncertainty also brings the risk of setting margins too thin and having a higher-than-anticipated failure rate in service, like Nissan's battery degradation in the first-generation Leaf, GM's spectacular failure with the LG pouch cell partnership for the Bolt and Ford's melting high-current electrical contacts in the Mustang Mach-E. In the fog of misunderstanding, mistakes happen, especially in organizations where decisions are often made based on politics, deceit and confrontation instead of logic, honesty and cooperation.
Drew Baglino discussed this in his Stanford interview earlier this year, saying that a decade ago Tesla had been too pessimistic about Model S battery cell electrochemical degradation, but too optimistic about the other stuff like pack moisture sealing, battery management electronics, mechanical shock and vibration, and thermal cycling. Notably, Drew said that these things "don't show up until you've been in the field for ten years". Yikes. So even the engineering geniuses at Tesla were too conservative in some areas and too aggressive in others. It was only after years of vehicles being in the fleet and millions of cars produced that they’ve advanced this far in fixing these problems, making the cars with more reliability, more quality, and less design fat.
Any new car company must learn all this from scratch. Sure, they can hire people who have worked in the car industry and they can do their best to copy industry best practices, and they can even buy all the latest commercial off-the-shelf software tools, but there's still a limit. Companies have institutional knowledge, policies and procedures, relationships between people, and habits that are hard to transfer over bit by bit to a new company. Tribal knowledge tends to be indigenous to the environment of the tribe. Companies also have critical data that they’re generally unwilling to share. Any legacy car company must learn all the EV-related stuff from scratch, and unfortunately for these laggards, it turns out that making a top-notch EV requires redesigning most of the car and factory, retraining workforce, and making a bunch of changes to the supply chain.
Tesla also gets the most data per car per unit time than anyone else, largely because they actually had the foresight to design the car for remote data collection and cloud computing. Tesla has been putting electronic sensors on their EVs since *2003*. One of the very first things they did as a startup was setting up vehicle data collection for trying to reverse engineer the AC Propulsion tzero prototype. At the 2016 Meeting of Shareholders, Elon, JB and several other early Tesla employees gave an extended presentation on the history of Tesla. If you’re a TSLA investor or Tesla enthusiast, I highly recommend watching to see more of “soap opera” story of Tesla and how they ended up on the path to vertical integration and rapid iteration. Video link below plays at timestamp where the history presentation starts.
At one point in the video Drew Baglino referred to the early days as trying to tease out “the ghost in the machine”, because the tzero used custom analog power electronics and nobody really knew much about how the hand-crafted, poorly documented mule actually functioned, including the folks at AC Propulsion.
All of this means Tesla—and Tesla alone—has the luxury of running the tightest tolerances in the industry for their EV designs, because no one has has produced 3 million EVs over the last decade. In other words, Tesla is the group of nerds who love the subject matter, spent all quarter studying together, and actually did all the homework so they’re ready to crush the exam easily while the competitors are the kids who cared more about playing popularity games and looking cool and are cramming a few days before the exam.
Imagine going camping in the wilderness. A novice might be a smart and conscientious planner, but their unawareness of the actual needs of the trip will inevitably result in worse selection of supplies to bring compared to a person going on the same trip who’s done it many times. The novice will bring along some stuff that’s unnecessary and not bring (or not bring enough of) other stuff that they actually do need. The expert also will have a better understanding of which equipment suppliers have the best options. The expert knows what to spend money on and what to go cheap on. The novice needs to spend more time researching and shopping and even then they will probably end up wasting money in some areas and get junky equipment for other items. The expert’s advantage is information and experience.
Novices can surpass experts in the long run. Tesla sucked at making cars 10 years ago, but that was with some prior learning on the original roadster and Tesla-level pace of innovation. This is not normal progress over the first decade of attempting to grow to being a mass manufacturer of cars. I don’t anticipate an iCar or any other competition having a meaningful negative impact on Tesla’s business for at least ten years.
Tesla’s data and expertise lets them eliminate waste in areas like:
Structural material
Welds
Fasteners
Battery cell depth-of-discharge reserve
Warranty reserve
Tesla also gets performance gains, such as:
Energy efficiency and range
Charging speed
Weight
More storage space and cabin interior space
Handling
More repeat sprints before motor power needs to be throttled
NVH (Noise, vibration & harshness)
Tesla's inventions compound each other's gains due to these feedback loops, augmenting Tesla's resultant lead.
For example, weight reduction and chassis stiffening can reduce the power required to move the vehicle around, reduce NVH, and improve handling. The improved energy efficiency not only improves range directly but it also enables reduction of the battery size needed which further reduces mass and energy consumption a bit more. Weight reduction also in many cases increases cabin storage space by opening up more room, as Tesla has shown with their masterful gigacasting design making for more spacious trunks and frunks. Better understanding of battery degradation and better thermal control means that a more aggressive charging curve can be allowed. And so on.
Examples of tech with compound benefit:
Octovalve, heat pump, Supermanifold and integrated thermal management across all vehicle subsystems
Gigacastings with special new aluminum alloy
Structural battery with seats directly mounted on top
Cell-to-pack architecture
Motors best in the game according to Munro & Associates testing and cost accounting
kW/$
kW/kg
kW/cm^3
4680 batteries
Aerodynamics
Cybertruck folded, stainless steel, stressed-skin structure
I don't think it's physically possible for a competitor to try all of this stuff in their first EV and succeed without a long string of improbable miracles. They have a long road ahead of them to implement these technologies that are necessary to have a product that can compete with Tesla vehicles on specs, features and cost.
Even the Tesla team themselves are still learning how to optimize their own inventions. Look at what they said on the Q2 2022 earnings call:
Elon Musk:
So structural pack where we get dual use of the battery cells as structure and as energy storage in the same way that an aircraft gets dual use of the wing as a fuel tank and as a wing is, I think, unequivocally, from a physics standpoint, the superior architecture. It's the A architecture. Now because it is new, we'll start off getting, I don't know, aspirationally a C within an A architecture.
But the potential is there for to get radically better and then unequivocally better than a battery pack, which is carried like a sack of potatoes.
Drew Baglino:
Yes. And we've gained the perspective through putting our first structural pack in production that it is actually the A architecture. Like before we did that, it was a hypothesis that was backed with a lot of modeling and first principles analysis. And now we've actually built it and are more confident in that assertion.
…
Drew Baglino:
Getting to the optimal design, right? Like you always start with some excess. Some people might call it fat, but that's not really what you think it is initially. It's that you don't know how lean you can get it until you've done it a couple of times.
Elon Musk:
Yes. I mean there's some platonic ideal of the perfect product where the atoms -- you have exactly the right atoms and they're in exactly the right position, and you asymptotically approach this platonic ideal. But it takes a lot of effort over time to figure out actually what is the platonic ideal and then actually gradually approach that.
Drew Baglino:
Yes. I mean, you might need to create a new alloy. Then you need to figure out how to cast it, then you need to ramp the casting machine with the new alloy.
…
Drew Baglino:
Yes, I was going to say the same thing, right? Like we're not just evaluating the pack in isolation either. It's the pack plus the body, the integration, do we have mass in the right places, do we have the cost in the right places and only just the right amount. And I think we've gone through one iteration. We're going to do another one with Cybertruck. I mean, we're taking the learnings and doing. The next version hopefully is a B-plus in A architecture. That's certainly a target.
Preferential Attachment
The Rich Get Richer
All signs point towards acceleration of Tesla's pace of technological innovation. I think Tesla is in a runaway snowball effect situation now. The EV market has an accumulative advantage dynamic with strong preferential attachment effects. Preferential attachment means a tendency within a competitive system for resources to be biased towards flowing to entities that already have more resources than other entities (i.e. "the rich get richer" / "success breeds success").
That was a lot of big words, so watch this easy and fun introduction to the topic from Vsauce and then reread the paragraph above.
Preferential attachment was first formally described scientifically in the 19th century by Vilfredo Pareto, the Italian economist/engineer/sociologist who famously observed that 80% of the peas in his garden came from 20% of the plants and 80% of the wealth and land in Italy was owned by 20% of the families. The “80/20 principle” is a special case of the broader “Pareto principle” named in his honor. Some pea plants in his garden gained an early advantage due to genetics or lucky position in the environment, and this tipping point effect made them grow bigger more quickly as sprouts than the other sprouts, and the plants leveraged this small advantage to consume more of the local sunshine, water and root space to increase their competitive advantage, until this minority of plants dominated the garden. This exponential relationship shows up in a surprising variety of phenomena, such as sizes of stars and planets formed from dust after a supernova, populations of countries, sizes of craters on the moon, frequency of words used in any language, and much more.
Preferential attachment usually results in a power law distribution, also known as a Pareto distribution. The discrete, non-continuous version of this is called a Zipf distribution, also known as a zeta distribution. There are theoretical mathematical justifications for why this happens, and if you want to see the calculus then I recommend exploring the Wikipedia link. The stronger the preferential attachment effect, the steeper the Pareto curve is. The inverse is true as well; whenever there is a Pareto distribution in the results of a competition, such as selling electric vehicles, we can be confident that some kind of preferential attachment effect probably dominates the dynamics.
In some cases, we observe power law rank relationships in which one or two extreme outliers exist at the top of the rankings, way off the trend line. This is called the “King effect”. Kings don’t conform to the exponential statistical rank distribution of the rest, like how China and India have exceptionally large populations while all other nations fit neatly into a Zipf curve, or how the United States by itself has 17 of the top 20 companies in the world with the highest market capitalization. King effects happen when there’s not only a preferential attachment dynamic, but also strong additional advantages specifically accruing only to the entity in the #1 spot.
Brand names can have this kind of effect, for example. When the average person thinks of facial tissue, what single word are they overwhelmingly most likely to think of? Kleenex. When the average person thinks of electric cars, what single word dominates? Tesla.
Another example is that Tesla’s competitors can’t even advertise their own EVs without driving comparison shopping with Tesla cars and ultimately sales to Tesla, as demonstrated by the spike in Tesla orders in the USA the day after the 2022 Superbowl in which there was a frenzy of electric vehicle ads.
Tesla has similar King Effect leads in miles of autonomous driving training data, total kWh of battery storage deployed, total CEO/Technoking social media followers and engagement, total mentions in new media, and much more.
The EV industry in the US, Tesla's home turf, shows a typical Zipf distribution with one king, Tesla, which alone still holds most of the US EV market share, and holds probably nearly all of the profit since everybody else is most likely selling EVs for approximately zero gross margin in order to meet Corporate Average Fuel Economy regulatory requirements. Soon enough, Tesla will have more profit than all of the rest of the auto industry combined, including all cars, not just EVs.
Virtuous Cycle Summary
Tesla has the lead in scale, data, talent and experience giving better products that cost less
--> Attract customers, investors and employees
--> More scale, more capital
--> Faster iteration cycles, more fun at work
--> More data and experience
I am about 99.9% all-in on Tesla via TSLA stock and call options. These are notes for my model that I’m sharing. I fully believe what I have written and I’ve put all of my money where my mouth is, but this is not advice regarding your personal investment or financial decisions. I'm not a certified professional investment or financial advisor and even if I were one, broad buying or selling advice would be inappropriate because there are so many variables to take into consideration such as your goals, risk tolerance, financial outlook, income, age, tax situation, dependents, and so on. Make your own choices. I hope one of those choices is to share this article with everyone you know, but that’s up to you. Thanks for reading.
Excellent summary Saxon - traditional auto either dismissed Tesla to failure, or assumed that they could pivot to EVs much faster than they actually were able to. And traditional auto greatly underestimated Tesla's ability to vertically integrated, thereby reducing dependence upon suppliers, and capturing that portion of profit not available to most auto manufacturers. We have a very exciting decade ahead of ourselves.