A typical car company:
- may make engines
- most parts, software made by suppliers
- dealerships do sales & services
-> mainly assembling
$TSLA: the above, and
- electric powertrains
- battery packs
- super charge network (= gas station)
- Tesla OS software
- AI chip
- FSD
$TSLA moats:
- best manufacturing
- best electric motors
- best batter pack density
- largest supercharger network
- best data for FSD
- vertical integration -> faster rate of innovation
- best CEO that can drive product innovation vested.co.in/blog/tesla-str…
$TSLA's manufacturing
- Tesla's factory: machines that make machines
- factory is the competitive strength of Tesla long-term
- giant casting machines -> make cars in the same way that toy cars are made
These are the reasons why Tesla dominated the list of most American-made cars (2022).
Tesla benefits from being highly vertically integrated while the rest of the industry has focused on vehicle bodies, assembly, and engines while relying on suppliers for much of the rest.
This year, Tesla is focusing on scaling up EV to capture market share while demand is skyrocketed. With more cars on roads, $TSLA has better chance of achieving FSD. FSD then leads to robot taxi and AI robots.
➡️massive value added to Tesla car owners (and $TSLA share holders)
To solve FSD, Tesla will need to solve real world AI:
✅silicon neural nets (brains)
✅cameras (visions)
A robots on 4 wheels problem (FSD) can be generalized into robots on legs.
Tesla humanoid robots can be bigger than car business, according to @elonmusk.
$TSLA is transforming into Robotics & AI company, while competitors are still having a hard time scaling up production.
✅Tesla - converging of AI, Robotics and Energy Storage
$TSLA FSD latest updates:
- we rearchitected the neural net for 1000 times
- radar & ultrasonics were a mistake
- 2 things are taking most amount of Elon's brain space: self-driving and star ships to orbit
- Tesla will probably have at least 5 year lead over competitors
The above moats translated into super high margin, $TSLA is on its own league comparing to other car makers.
✅ $TSLA >= $AAPL for integrated hardware & software products that people love.
✅ $TSLA >= $TSM in manufacturing efficiency and high margin.
✅ $TSLA >= $GOOG in AI.
$TSLA's gigafactories, why it's so hard for competitors to catch up:
- the factory is the product
- most advanced car factory the earth has ever seen. Alien technology (@elonmusk on Giga Texas)
- the factory is like a chip, raw materials in one side, cars out the other side
$TSLA's manufacturing expertise in batteries as competitive edge:
- eliminate steps
- streamline processes
- slash costs
- battery factories next to car plants and chemical plants
- Tesla’s new 4680 battery cells are far cheaper and can store far more power per unit of volume.
$TSLA high margin:
- lower batter cost
- direct sales
- no marketing expense
- simpler design with fewer options
- scale automation in manufacturing
- software, FSD
"People who are really serious about software should make their own hardware." - Alan Kay
$TSLA has been very serious about software and vertical integration, that why it overcame the chip shortage while legacy car makers struggled.
The unified computing architecture made it possible for Tesla to rewrite its software to work with chips that were not in short supply.
$TSLA also took control of their FSD destiny by replacing $NVDA with its own AI chip (manufactured by #Samsung).
@elonmusk, 2019: Telsa designed the best chip in the world, best by a huge margin. Tesla claimed a 21x gain in perf over the previous Nvidia's chip & with 80% cost.
How it was possible for Tesla to design the best AI chip, while it had never designed a chip before?
That's because of Elon's clear vision on what he wanted, and the 'how' was solved by legendary chip designer Jim Keller.
$TSLA also built its own NN training supercomputer, Dojo, with its 2nd chip, the Dojo D1. The chip was designed by Tesla all the way from the architecture to the package.
✅best AI training performance
✅enable extremely complex neural net models
✅power and cost-efficient
$TSLA has built the complete integrated ecosystem for its FSD from battery, car design, advanced manufacturing at scale, software, chip, and AI training.
Take a step back to see where FSD is in Tesla's complete energy and transportation ecosystem.
$TSLA Dojo could become the best super computer for AI that ever developed.
Tesla will use Dojo to train its neural networks FSD, but as mentioned by @elonmusk, Tesla could open it up for other developers as well, and that could potentially become a new business for Tesla.
$TSLA's FSD focuses on solving computer vision problem, which is different from most other companies like Waymo that also use radar, lidar, etc.
@elonmusk famously said: Lidar is a fool’s errand.
@Mobileye creates 2 independent models, one from cameras, one from radar & lidar.
$TSLA FSD has another big, major advantage: real world data, and quality of data.
Telsa's cars are everywhere and increasing rapidly. This leads to the flywheel effect that will further distance Tesla from competitors, just like how $GOOG is dominating the search engine market.
Why Lidar is doomed:
❌expensive
❌useless in bad weather conditions
❌power hungry
❌ugly
❌can't differentiate objects
❌needs HD maps
With data, $TSLA can replace Lidar.
✅Competitors are wasting time and resources
➡️ $TSLA is now at 5+ years lead in AV.
Why it's possible for $TSLA to remove Lidar and radars?
- object detection, with depth, velocity, and acceleration by deep learning (supervised learning)
- NN learns to detect objects and their associated properties
- a combination of auto & manual (human cognition) labeling
$TSLA deep learning:
@lexfridman: “For Waymo, deep learning is the icing on the cake; for Tesla, deep learning is the cake.”
DL needs data.
All new Teslas are equipped with the FSD hardware. Tesla in fact filed patent for sourcing self-driving training data from its fleet.
$TSLA FSD vertical integration:
✅manufacturing cars and the hardware for FSD
✅video data from millions of cars
✅supercomputers to train deep learning models
✅AI chips installed inside cars
✅validates AI through shadow testing
Tesla owns the entire self-driving car stack.
$TSLA AI - while solving autonomous driving (AV), Tesla has become the leader in real world AI.
🔜Tesla is becoming an AI company.
✅Neural net
✅Autopilot
✅Dojo
✅Data
✅Simulation
✅Tesla Bot
Learn more about Tesla AI:
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Looks like Elon Musk is interested in having another compensation package from Tesla.
My thoughts:
•The same group of people who said Elon should still focus on Tesla after acquiring Twitter now say he needs motivation to work hard for Tesla.
•Elon is worth about $230 billion, including ~$100 billion in Tesla ($TSLA) stock already. Wouldn’t that be motivation enough to 10x that money?
•Market capitalization seems to be the most flawed goal; this may encourage bad behavior to hype the stock. Remember, one of the main bullish theses for Tesla was the Robotaxi. It has been just a thesis for the last ~8 years and still no clear sign of being fully accomplished.
•It seems some people want Elon to boost the stock price more than anything else.
•Jensen Huang’s pay package is very small, and no one needs to question his motivation for Nvidia. $NVDA
As a big fan of Elon Musk and Tesla, I've been closely following Tesla's AI progress. A defining moment last year was when Elon said he was still buying as many H100s as possible, indicating that Tesla is constrained by computing power.
I tried to decode what this means and reasoned it out using first principles.
I learned that despite spending billions of dollars and several years, Tesla's Dojo is still far from being able to replace Nvidia's A100 & H100s, suggesting that Nvidia must have done a lot of things right.
This made me think that having a fully vertically integrated system is good for Tesla when it can move faster than the industry, but it can be detrimental if it's slower (i.e., Dojo < H100s) or if the chosen solution is incorrect (e.g., whether relying solely on vision is right).
One advantage Tesla has over others is its access to real-world data.
But, thinking from first principles, this can be solved through simulation, or considering that the industry collectively has more cars than Tesla alone.
I concluded that Tesla's Full Self-Driving (FSD) doesn't have a 100% success rate, but Nvidia does. No matter the direction of the industry, they have something valuable to offer.
Let's look at Nvidia's autonomous platform.
Nvidia's DRIVE® end-to-end computing full-stack platform for autonomous vehicles.
Enable anyone to build anything autonomous
In-car solution
DRIVE Hyperion is the in-car solution, a vehicle architecture that includes sensors, DRIVE AGX for compute, and software tools necessary for robust self-driving and intelligent cockpit capabilities.
In the data center
NVIDIA provides the hardware and software you need for AV development, including NVIDIA DGX™ to train DNN for perception and DRIVE Sim for generating data sets and validating the entire AV stack.
Open and flexible
It offers various levels of engagement for customers.
They can choose to purchase just the chips, or chips along with algorithms. There's also the option to use the operating system or the Omniverse virtual world simulator for synthetic data generation and testing robots in a virtual environment. This openness has led to customers working with the company across these different areas. $NVDA $TSLA
One prime example of Nvidia's partnership with Foxconn, the world's largest manufacturer.
Foxconn will integrate NVIDIA technology to develop “AI factories”, a new class of data centers. These AI factories will be based on the NVIDIA accelerated computing platform, including NVIDIA GH200 and NVIDIA AI Enterprise software.
Applications of AI Factories:
- Digitalizing manufacturing and inspection workflows.
- Developing AI-powered electric vehicles (EVs) and robotics platforms.
- Expanding language-based generative AI services.
Additional Collaborations:
Foxconn Smart EV:
To be built on NVIDIA DRIVE Hyperion 9, a next-gen platform for autonomous automotive fleets.
Powered by NVIDIA DRIVE Thor, NVIDIA's future automotive System on Chip (SoC).
Foxconn Smart Manufacturing:
Robotic systems will be built on the NVIDIA Isaac autonomous mobile robot platform.
Foxconn Smart City:
Will incorporate the NVIDIA Metropolis intelligent video analytics platform.
One important aspect that many people may overlook:
No one needs to code 'autonomy logic' anymore; it's now a process of data in, intelligence out.
Elon mentioned the same thing about Tesla FSD V12. But what does that mean?
It implies that there is no longer a 'first mover advantage,' as all the previously hardcoded logic is now obsolete.
It's all about data and training infrastructure.
☑️ In terms of training infrastructure, those who purchase the most of Nvidia's AI Factories (DGX) will have an advantage.
☑️ Regarding data, the advantage will go to those who sell the most cars, even inexpensive ones. Additionally, they can use Nvidia's SIM based on Omniverse for simulation data.
We have been talking a lot about Tesla’s Full Self-Driving, but what’s the difference between Tesla FSD and Nvidia’s Self-Driving Platform?
Nvidia’s strategy is not just to build self-driving cars, but to enable the entire world to make anything that moves fully autonomous.
The result is an end-to-end computing full-stack platform.
‘Enable anyone to build anything autonomous’
Jensen Huang explained his big vision here.
BYD and NVIDIA
BYD, the world’s largest electric vehicle manufacturer, is building its next-generation vehicles on the NVIDIA DRIVE Orin™ platform. These programmable fleets will be software-defined and powered by a centralized, high-performance computer that enhances the vehicle over time through over-the-air updates.
Mercedes-Benz and NVIDIA
Mercedes-Benz has partnered with NVIDIA to bring artificial intelligence (AI) and metaverse technologies into its design and development process. This collaboration will use NVIDIA DRIVE Orin™ to create software-defined vehicles and NVIDIA Omniverse™ for more intelligent and efficient manufacturing, bringing unprecedented advancements to the automotive industry.
Jensen Huang on digital biology: ‘It’s the next revolution. It’s going to be flat-out one of the biggest ones ever. And Nvidia is at the center of it.’ $NVDA
Digital biology, also known as computational biology, refers to the use of data-intensive computational methods to understand biological systems and processes. It encompasses a range of fields including genomics, proteomics, bioinformatics, and systems biology.
The core idea is to apply algorithms, computational models, and data analysis techniques to biological data.
Nvidia plays a central role in digital biology for several reasons:
1. High-Performance Computing (HPC) Needs:
Digital biology requires immense computational power to process and analyze large datasets, such as genomic sequences. Nvidia's GPUs are highly efficient at parallel processing, making them ideal for these computationally intensive tasks.
2. Machine Learning and AI:
Nvidia is a leader in AI and machine learning technologies, which are increasingly important in digital biology for tasks like pattern recognition, predictive modeling, and simulation. Nvidia's CUDA (Compute Unified Device Architecture) platform allows for efficient programming of complex algorithms that can be accelerated on GPUs.
3. Data Visualization:
Nvidia’s technology also supports advanced data visualization, crucial for interpreting complex biological data.
4. Collaborations and Research Support:
Nvidia often collaborates with research institutions and biotech companies, providing the necessary hardware and software support for cutting-edge biological research.
5. Cloud Computing and Edge AI:
Nvidia's technology enables faster and more efficient processing of biological data both in centralized data centers and at the edge (e.g., in remote research facilities).
NVIDIA Clara™ is a suite of computing platforms, software, and services that powers AI solutions for healthcare and life sciences, from imaging and instruments to genomics and drug discovery.
Thanks @ItsRor for pointing it out.
NVIDIA BioNeMo™ is a generative AI platform aimed at enhancing drug discovery. It facilitates easy and scalable training of models for various drug discovery stages, from target identification to lead optimization. The platform includes workflows for 3D protein structure prediction, design, virtual screening, and more. BioNeMo provides tools for large-scale model training and simplifies the management of AI supercomputing resources. Its optimized framework, pretrained models, and user-friendly interface enable efficient AI model development and deployment, significantly accelerating AI-powered drug discovery.
According to IOC Analytics, Nvidia holds a 92% market share in data center GPUs.
If Lisa Su is correct, the market could be as large as $400B by 2027. Assuming Nvidia retains a market share of only 70%, with fierce competition from all sides, Nvidia would generate approximately $280B in sales per year.
However, considering that Lisa Su is selling AMD's GPUs, let's assume the market is only $300B. In that case, Nvidia would have $210B in data center sales.
"The NVIDIA A100 Tensor Core GPU is the de facto standard for data center GPUs. However, as discussed in the report, hardware is not the only differentiator for NVIDIA. Some consider their developer ecosystem, CUDA, as NVIDIA’s biggest moat, and it is often cited as the key reason why NVIDIA is not set to lose its dominant position anytime soon." - IOC Analytics