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

I am a fan of Elon but I dont get it.

This is Jensen Huangs compensation package.

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.

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.

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.

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 iot-analytics.com/leading-genera…

NVIDIA GPU supply issues Involve Packaging, Not Chip Wafers.

Nvidia's H-class GPUs make use of TSMC's 2.5D Chip-on-Wafer-on-Substrate (CoWoS) packaging technology.

TSMC itself has said that it expects it to take 1.5 years to bring the packaging process backlog back in line. Why H100?

Compared to the A100, the Hopper "H100" brings in 3.5 times more 16-bit inference and 2.3 times 16-bit training performance.

Elon Musk once said that H100 was 3x faster than A100 in Tesla's FSD training test.

The fall of Apple iphone market share.
- peaked at ~40% in 2009
- kept falling none stop to 20% in 2015 and stayed at 20% since then

Does that sound similar to Tesla in EV market share? More below👇

$AAPL $TSLA Apple iphone market share peaked in H1 2009 and then falling, the stock should fall too, right?

But no, $AAPL was up 10x in the next 6 years from 2009 to 2015

$TSLA - problems with low stock price
1. Cashflow can be affected due to more cash needed for compensation
2. Employee morale is lowered since they see the loss in value of their granted stocks by 70%
3. Tesla's brand may be impacted negatively @MartinViecha @elonmusk @kimbal I have no doubt that Elon doesn't care about his paper loss or even money, but a lot of employees would be upset with say $1M stocks awarded suddenly became $300k in a few months.

This is why Elon had to send an email to reassure Tesla employees and blamed the market craziness.

Market Forward PEG [Updated Dec 30, 2022]
☑️ S&P 500: 1.7
☑️ S&P 500 Tech: 1.9
✅ Tesla: 0.57

$TSLA fair market price = 1.9 * 40 * $5.38 = $408

Tesla analyst estimate:
- 2023 EPS: $5.38 (from $5.6)
- 5y growth: 40% (from 48%)

$TSLA 5y forward PEG = ($123/5.38)/40 = 0.57 With S&P 500: 1.7 let's see how market expected $TSLA to grow EPS:

$TSLA EPS 2022E: $4.03
$TSLA EPS 2023E: $5.38
$TSLA PPS = $123

✔️ $TSLA expected 5y growth rate from 2023 = ($123/$4.03)/1.7 = 18%

✔️ $TSLA expected 5y growth rate from 2024 = ($123/$5.38)/1.7 = 13.5%

10 reasons to sell Tesla $TSLA $TSLAQ 👇 1. $TSLA is overvalue, PE is higher than $AAPL

Using PEG metric, $TSLA is in fact A LOT cheaper than most other mega stocks including $GOOG $AAPL $MSFT $NVDA etc.

https://twitter.com/The_AI_Investor/status/1580092064272089088

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…

$PATH, UiPath Inc.
- Market leader in the Robotic Process Automation (RPA) software market
- Software robots that use AI, Computer Vision, and RPA to emulate people
- Massive & fast growing TAM when automation is changing how work is done
- ARR: 726.5M, 60% up Y/Y
- NRR: 144% UiPath robots:
- See: use AI/Computer Vision to read the screen
- Think: use AI/Machine Learning to decide what to do
- Do: RPA interacts with applications via UI/API
As the leader, UiPath has the most data -> data network effects.

$STEM Stem, Inc.
- Leader in AI-driven energy storage solutions
- Massive TAM and strong macro tailwinds
- First mover AI platform: operates with 40+ utilities, 5 grid operators
- Athena AI software generates 30%+ more savings
than the competition
- Recurring SaaS model, ~ 80% GM Athena AI
- The Edge Platform continuously collects extensive data from meters, breakers, energy storage and solar generation systems and conducts local, real-time control.
- The Athena Cloud Platform processes data provided by a network of Stem’s edge devices and other sources.