$TSLA up 3.4% - people bicker
$TSLA down 3.4% - people bicker
Hate to see what happens when $TSLA has one of those 10% up days again
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Tesla, TSLA & the Investment World: the Perpetual Investors' Roundtable
- Thread starter AudubonB
- Start date
$TSLA down 3.4% - people bicker
Hate to see what happens when $TSLA has one of those 10% up days again
The chargers themselves are also a lot more complicated. As I mentioned a couple of days ago, Electrify America uses three different vendors for their chargers. So that means maintenance crews have to know how to service and stock spare parts for all three.
Also, Electrify America's cables are liquid-cooled. It has pumps that start up when you start charging. So EA chargers have moving parts that wear out more easily. And it means the cables are more bulky and harder to plug in to the car. But that's how EA was able to offer 350kW speeds long before Tesla. However, four years after EA's rollout of these 350kW chargers, we still have no cars that can take advantage of it.
This is just another example of Tesla's vertical integration and simplicity. It results in reliability.
When we go up >10% we thank each other for how helpful and supportive this group has been.$TSLA up 3.4% - people bicker
$TSLA down 3.4% - people bicker
Hate to see what happens when $TSLA has one of those 10% up days again
Seriously, I love this group...
snellenr
Member
He pointed out (@3:12) the crush cans bolted to the front casting that allows less energetic frontal impacts to be repaired. But yeah, any impacts severe enough to damage the castings will total the car.
The gigacastings use a slightly tweaked AA386 alloy with about 8.5% Si content. Nothing particularly magical about it, though it does highlight Tesla's metallurgical prowess.
Anyhow, Tesla has the chromatography and other analysis equipment to determine what adjustments are needed to get the right blend. Not exactly rocket science. Well...
People start talking about buying islands again.$TSLA up 3.4% - people bicker
$TSLA down 3.4% - people bicker
Hate to see what happens when $TSLA has one of those 10% up days again
On 20% days I think people talk about refreshing the SpaceX page to see when they start offering Starship seats to the public.
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Something I've been wondering about for a while. With Tesla adding CCS charging for non-Tesla vehicles, hopefully with the same degree of uptime as the have had in the past with existing Supercharger network, will the become the de facto charging network industry-wide? Essentially the "Exon" of EV charging, eventually displacing companies such as EVGO, EA and Chargepoint, due to much better service? And if that happens, how much of a profit center does Supercharging become? Now I see it as somewhat of a necessary expense to support the auto lineup (not sure if they currently turn a profit on charging). But in time it seems as if that should be quite profitable.
Well, they pretty much have the right of way because hitting one will ruin your entire day. Unless you're in a locomotive-which take out a lot of moose and deer every winter. Lug nut rule.
That's funny, I was thinking the same thing, should have included it. Even though moose have been around longer than the locomotive, ain't no way they can avoid one.
I worked on a billing data integration with ChargePoint on one of my Utility projects. We went round and round for a few months trying to get them (the vendor) to supply the data in a format we could easily consume in a timely manner. They simply couldn't get do it so we had to workaround the issue. This was pretty simple data too.
Software. They have crap software. That's the answer.
Well, they can, but they don't. Instead of just walking off the track into the surrounding woods, they run down the track. I run into in them in the woods too (as in encounter, not literally run into)-instead of hanging in the woods they stay on forest service roads-and are very hesitant to get off. Even when "pushed", they run along the road instead of bailing. I've passed them a few times.
I wrote my reply incorrectly, I meant that the train couldn't avoid the moose, sorry it read the other way.
Gigapress
Trying to be less wrong
Giving Tesla a 15-year head start was a bad idea
Being the first mover in a market has advantages and disadvantages. Advantages include 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 not just an industry-wide model; it also applies to individual firms. The game is actually a race down the Wright’s law learning curve, as well as a competition over whose curve has the steepest slope—that is, who extracts the most innovation potential out of each doubling of cumulative production. 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 engineering, including 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.
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, 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. Engineers also have models and test data for the manufacturing system and associated uncertainty. Automotive engineering has even more uncertainty than most machine designs because the duty cycle is intense, customer expectations are high, and the vehicle spends most of its time outside with all the accompanying stress from vibration, temperature, salt, moisture, even UV radiation. Plus, the expected service lifetime is more than a decade. Accelerated life testing is crucial for planning this but you just never really know until actually putting the cars in service and waiting for them to get old.
Any new car company has to 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.
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 f-up with the LG pouch cell partnership and Ford's melting high-current electrical contacts. In the fog of misunderstanding, mistakes happen, especially in organizations where decisions are 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 big brains 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.
Tesla also gets the most data per car per unit time. 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 BEVs 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 t-zero prototype. I heard Elon and a few other early Tesla employees talking about this in a panel interview from around the early Model S years (I can’t find it anymore, so no link). I think I recall Elon referring to it as trying to tease out “the ghost in the machine”, because the t-zero used custom analog power electronics and nobody really knew how the hand-crafted mule actually functioned.
All of this means Tesla alone has the luxury of running the tightest tolerances in the industry for their BEV designs, because no one has has produced 3 million BEVs over the last decade. This is like 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 get by with less stuff such as:
Tesla's inventions compound each other's gains due to these feedback loops, augmenting Tesla's resultant lead.
Weight reduction and chassis stiffening, for example, reduces the power required to move the vehicle around, reduces NVH, and improves handling which makes the vehicle more efficient, which then enables reduction of the battery size needed for a given set of requirements for range and performance. 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.
Example of tech with compound benefit:
Even Tesla is still learning how to optimize their own inventions. Listen to remarks from the Q2 call:
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"). Preferential attachment was observed by Italian economist/engineer/sociologist Vilfred Pareto in his famous observation 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 early advantage gained by some pea plants due to genetics or lucky position in the environment made them grow bigger more quickly as sprouts, and they leveraged this small advantage to consume more of the local sunshine, water and root space to grow even bigger, until a minority of plants dominated the garden. This relationship shows up in all kinds of phenomena like formation of stars and planets from dust after a supernova, crater size 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. There are theoretical justifications for this and if you want to see the math I recommend reading the link. The stronger the preferential attachment effect, the steeper the Pareto curve is. Whenever there is a Pareto distribution in results of a competition, we can be pretty confident that some kind of preferential attachment effect exists.
In some cases, we observe power law rank relationships in which one or two outliers exist at the top, way off the trend line. This is called the king effect. Kings don’t conform to the statistical distribution of the rest, like how China and India have exceptionally large populations while all other nations fit neatly into a Pareto curve.
The EV industry in the US, Tesla's home turf, shows a typical Pareto distribution with one king, Tesla, which alone still holds most of the US BEV market share, and holds all of the profit. Soon enough they'll have more profit than all of the rest of the auto industry combined, including all cars, not just BEVs.
On a linear scale we can see just how far ahead Tesla is. Note that the pink colulmn is the grand total, the red column is Tesla, and the blue columns are the rest. On a logarithmic scale we can see that the power law model is a good fit, because all the data points fall appromixately in a line. All of them except Tesla, whose sales number comes in an order of magnitude higher than the power law rank relationship would predict.
Source: Inside EVs (link)
The dynamics that caused this result are not likely to change any time soon. The rank relationship for 2018 looks almost identical, again with Tesla an order of magnitude ahead of where the Pareto distribution of the rest of the market participants would predict Tesla to be. The numbers have gotten bigger and and the also-rans have shuffled around in the rankings, but in four years nobody has gotten any closer. In fact, if you look closely at the trend lines, Tesla’s deviation from the distribution has almost doubled since 2018, suggesting that indeed they are accumulating relative advantage over time.
Source: Inside EVs (link)
Tesla has the lead in data and experience giving better products that cost less
Being the first mover in a market has advantages and disadvantages. Advantages include 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 not just an industry-wide model; it also applies to individual firms. The game is actually a race down the Wright’s law learning curve, as well as a competition over whose curve has the steepest slope—that is, who extracts the most innovation potential out of each doubling of cumulative production. 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 engineering, including 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.
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, 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. Engineers also have models and test data for the manufacturing system and associated uncertainty. Automotive engineering has even more uncertainty than most machine designs because the duty cycle is intense, customer expectations are high, and the vehicle spends most of its time outside with all the accompanying stress from vibration, temperature, salt, moisture, even UV radiation. Plus, the expected service lifetime is more than a decade. Accelerated life testing is crucial for planning this but you just never really know until actually putting the cars in service and waiting for them to get old.
Any new car company has to 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.
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 f-up with the LG pouch cell partnership and Ford's melting high-current electrical contacts. In the fog of misunderstanding, mistakes happen, especially in organizations where decisions are 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 big brains 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.
Tesla also gets the most data per car per unit time. 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 BEVs 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 t-zero prototype. I heard Elon and a few other early Tesla employees talking about this in a panel interview from around the early Model S years (I can’t find it anymore, so no link). I think I recall Elon referring to it as trying to tease out “the ghost in the machine”, because the t-zero used custom analog power electronics and nobody really knew how the hand-crafted mule actually functioned.
All of this means Tesla alone has the luxury of running the tightest tolerances in the industry for their BEV designs, because no one has has produced 3 million BEVs over the last decade. This is like 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 get by with less stuff such as:
- Structural material
- Welds
- Fasteners
- Battery cell depth-of-discharge reserve
- Warranty reserve
- Range per kWh
- Charging speed
- Weight
- More storage space and cabin interior space
- Handling
- More repeat sprints before 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.
Weight reduction and chassis stiffening, for example, reduces the power required to move the vehicle around, reduces NVH, and improves handling which makes the vehicle more efficient, which then enables reduction of the battery size needed for a given set of requirements for range and performance. 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.
Example of tech with compound benefit:
- Octovalve and integrated thermal management across all vehicle subsystems
- Gigacastings with optimized new alloy
- Structural battery with seats directly mounted on top
- Cell-to-pack architecture
- Motors best in the game according to Munro testing and cost accounting (kW/$, kW/kg, kW/cm^3)
- 4680 batteries
- Aerodynamics
- Cybertruck folded stainless steel stressed skin structure
Even Tesla is still learning how to optimize their own inventions. Listen to remarks from the Q2 call:
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"). Preferential attachment was observed by Italian economist/engineer/sociologist Vilfred Pareto in his famous observation 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 early advantage gained by some pea plants due to genetics or lucky position in the environment made them grow bigger more quickly as sprouts, and they leveraged this small advantage to consume more of the local sunshine, water and root space to grow even bigger, until a minority of plants dominated the garden. This relationship shows up in all kinds of phenomena like formation of stars and planets from dust after a supernova, crater size 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. There are theoretical justifications for this and if you want to see the math I recommend reading the link. The stronger the preferential attachment effect, the steeper the Pareto curve is. Whenever there is a Pareto distribution in results of a competition, we can be pretty confident that some kind of preferential attachment effect exists.
In some cases, we observe power law rank relationships in which one or two outliers exist at the top, way off the trend line. This is called the king effect. Kings don’t conform to the statistical distribution of the rest, like how China and India have exceptionally large populations while all other nations fit neatly into a Pareto curve.
The EV industry in the US, Tesla's home turf, shows a typical Pareto distribution with one king, Tesla, which alone still holds most of the US BEV market share, and holds all of the profit. Soon enough they'll have more profit than all of the rest of the auto industry combined, including all cars, not just BEVs.
On a linear scale we can see just how far ahead Tesla is. Note that the pink colulmn is the grand total, the red column is Tesla, and the blue columns are the rest. On a logarithmic scale we can see that the power law model is a good fit, because all the data points fall appromixately in a line. All of them except Tesla, whose sales number comes in an order of magnitude higher than the power law rank relationship would predict.
| Total | 193481 |
| Tesla | 139338 |
| Ford | 11751 |
| Kia | 11483 |
| Hyundai | 9675 |
| Nissan | 5980 |
| Audi | 5100 |
| Volkswagen | 3527 |
| Mercedes-Benz | 2641 |
| General Motors | 1648 |
| Rivian | 1145 |
| BMW | 611 |
| Lucid | 582 |
The dynamics that caused this result are not likely to change any time soon. The rank relationship for 2018 looks almost identical, again with Tesla an order of magnitude ahead of where the Pareto distribution of the rest of the market participants would predict Tesla to be. The numbers have gotten bigger and and the also-rans have shuffled around in the rankings, but in four years nobody has gotten any closer. In fact, if you look closely at the trend lines, Tesla’s deviation from the distribution has almost doubled since 2018, suggesting that indeed they are accumulating relative advantage over time.
| Total | 239003 |
| Tesla | 191627 |
| General Motors | 18019 |
| Nissan | 14715 |
| BMW | 6889 |
| Fiat | 2250 |
| Volkswagen | 1354 |
| Smart | 1219 |
| Kia | 1134 |
| Honda | 948 |
| Jaguar | 393 |
| Hyundai | 345 |
| Ford | 70 |
| Mercedes | 40 |
Tesla has the lead in data 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
--> Better products that cost less
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@Gigapress - you are not a human, are you? It is okay to tell us. ^Great, great post BTW
Gigapress
Trying to be less wrong
My understanding was that all of us are bots.
bkp_duke
Well-Known Member
He needs a venue to post this that is more than just a simple forum.
@Gigapress - if you want some free WordPress hosting or something, hit me up. I'll have my staff deploy a server for you and you can post to your heart's content. Everyone here can then plaster it all over the internet.
Knightshade
Well-Known Member
So just like most other days.
Honestly if we restricted comments on capping to days it's NOT happened it'd reduce the # of posts significantly.
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