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https://twitter.com/The_AI_Investor/status/1542381559168192513

@elonmusk

https://twitter.com/The_AI_Investor/status/1573831812488769537

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The AI Investor

@The_AI_Investor

Jan 15

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.

And this is Jensen's motivation for $NVDA

Read 6 tweets

The AI Investor

@The_AI_Investor

Jan 14

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.

Read 5 tweets

The AI Investor

@The_AI_Investor

Jan 14

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.

Read 6 tweets

The AI Investor

@The_AI_Investor

Jan 13

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.

Read 5 tweets

The AI Investor

@The_AI_Investor

Dec 24, 2023

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…

https://twitter.com/The_AI_Investor/status/1736648010069921974

Lisa Su: The AI infrastructure opportunity is expected to grow at a 70% CAGR from $45B in 2023 to $400B in 2027.

Jensen Huang: $1 trillion will be spent over 4 years upgrading data centers for AI.

https://twitter.com/The_AI_Investor/status/1736648010069921974

Read 8 tweets

The AI Investor

@The_AI_Investor

Dec 31, 2022

@MartinViecha

$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.

$TSLA stock declining may also give consumers a negative view of the company.

The public may see Tesla as a falling company. This also adds to the brand value concern over Elon's divisive political tweets.

We may not see the data yet, but when we see the data, it may be late.

Read 6 tweets

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