🔑 Key Takeaways
- NVIDIA's powerful GPU architecture is being utilized in various industries such as scientific computing, drug discovery, and climate modeling. Its potential is not just limited to gaming but also benefits enterprise data centers and self-driving cars. Vanta's products further improve cybersecurity for companies.
- NVIDIA's success in the graphics card market is a result of constantly pushing boundaries, investing in research and development, taking calculated risks, and prioritizing user experience despite increasing fixed costs.
- NVIDIA's investment in building an ecosystem for developers and SDKs opened up new markets beyond gaming, highlighting the importance of expanding beyond initial success and exploring new opportunities.
- Jensen Huang's commitment to building CUDA, despite lack of initial market demand, led to durable differentiation for NVIDIA and protected the company from competitors like ATI. Building a proprietary platform can be a wise investment for future success.
- CUDA is a paradigm shift towards parallel programming, with a powerful ecosystem that optimizes across the GPU, acceleration libraries, systems, and applications, impacting hundreds of industries.
- CUDA is a free, closed-source platform and programming model exclusively for NVIDIA's hardware. It's ideal for parallel executions and embarrassingly parallel applications, following the Apple business model of selling hardware with healthy gross margins. The launch of Tegra chip in 2008 was a crucial step towards commercialism.
- Despite successful ventures with Tesla and Nintendo, NVIDIA faces challenges in the highly competitive mobile GPU market due to the lack of profitability in the Android value chain ecosystem. Their growth may not be replicable as a model for effective strategy.
- NVIDIA's implementation of AlexNet on GPUs in CUDA was a pivotal moment in AI and deep learning history, enabling the use of parallel computing and ushering in the modern era of computing.
- Deep learning and NVIDIA's cuDNN library have transformed technology, paving the way for computers to recognize images, self-driving cars, and predicting user behavior. This change created a multi-trillion dollar market in digital advertising, with NVIDIA as the leading chip and software provider.
- Successful investments require a long-term vision and faith in emerging technology, like NVIDIA's early recognition of GPUs' potential for deep learning. Staying ahead of the curve and taking risks can make a big impact.
- Don't underestimate the potential uses for a product, as unexpected applications can arise. Understanding demand and use cases is essential for successful product development.
- NVIDIA's data center revenue has tripled in the past two years due to increased demand for machine learning hardware. High-end graphics cards are sold at premium prices, and the acquisition of Mellanox allows for improved connectivity. The enterprise is now buying solutions, not just cards, resulting in high gross margins similar to Apple.
- NVIDIA's DPU simplifies data processing and communication within data centers, acting as a third leg of computing alongside CPU and GPU. It allows for high abstraction writing and efficient handling of data movement, making data centers more efficient.
- Despite regulatory pressure on its acquisition of Arm, NVIDIA plans to leverage Arm's chip designs to expand its presence in the trillion-dollar data center market, aiming to capture 1% of the $100 trillion opportunity through innovation and expansion.
- NVIDIA's differentiated business model, including software and services bundled with hardware, generates $8 billion in free cash flow annually. The company's focus on targeting different segments and offering simple solutions reflects a strong segmentation strategy for growth.
- NVIDIA's DLSS technology and dedicated crypto mining cards have contributed to their impressive 60% growth rate and dominance in the gaming industry, surpassing even bigger companies like Apple and Microsoft.
- NVIDIA's Hyperion drive platform provides a comprehensive solution for automakers to integrate EV and AV technology while also creating new opportunities for NVIDIA to dominate the industry value chain.
- With NVIDIA's Omniverse, enterprises can test changes to real-world assets efficiently and with no human intervention, avoiding the risks and costs of testing in production. However, emerging competitors may disrupt the market in the future.
- NVIDIA's sustainable competitive advantage comes from scale economies, switching costs, and branding through its CUDA investment. Its horizontal position allows it to serve every customer while maintaining its CapEx advantage over vertical players like Google.
- Owning trade secrets and incorporating hardware and software is critical for success in the complex market of AI and deep learning. Adaptability to market trends and confidence in strategy are key attributes of successful companies.
- NVIDIA's capital-efficient business model and Softbank's potential for modernization in Latin America make them exciting investment opportunities for long-term success. Investing in technology and innovation can lead to overall prosperity.
- NVIDIA's focus on innovation and acquisition, combined with belief in real-world AI use cases, positions them for continued growth. Continued adaptation to redefine GPU as a solution for various workloads is key to remaining dominant.
- The Memories Legion complements the Expanse series while Sony RX100 and Acquired offer excellent tools for capturing real images and finding your dream job. Join the fun in Seattle on May 4th!
📝 Podcast Summary
How NVIDIA's Innovative Technology is Revolutionizing Industries Beyond Gaming.
NVIDIA has built hardware, software, and user-facing software to recreate the real world with an efficient and powerful GPU architecture. These building blocks are not just for gamers as they are making it possible to predict airflow over a wing, simulate cell interaction to quickly discover new drugs, or model and predict how climate change will play out. The scale of data and computing required to accomplish these tasks is unfathomable, and the fact that it happens on one graphics card is mind-blowing. Many industries, from gaming to enterprise data centers to scientific computing and self-driving cars, are benefiting from NVIDIA's innovative technology. Vanta's product helps companies get more secure by helping them prepare for compliance audits and fix security issues faster.
Lessons from NVIDIA’s Success in Graphics Cards Market
NVIDIA is an impressive company that has conquered the graphics card market by shipping new architectures at warp speed every six months and writing their own drivers for their graphics cards, ensuring quality and control over user experience. Despite taking on a bigger fixed cost base, they viewed it as a long term benefit for the company's growth and success. They continue to innovate and build capabilities that not many other chip companies have, attracting high-end gamers that want the best experience. The story of NVIDIA illustrates the importance of constantly pushing boundaries, investing in research and development, and taking calculated risks to build a successful company.
NVIDIA's journey from gaming to scientific and general-purpose computing
NVIDIA's success in the gaming market led them to develop a direct relationship with developers for building a developer ecosystem. While they could have just ridden the wave of the gaming market, Jensen had bigger ambitions. He wanted NVIDIA to serve the scientific and general-purpose computing markets, which was a massive undertaking. To achieve this, they needed to build a whole stack of SDKs that would make it easier for domains and disciplines to write software for NVIDIA's hardware. This required massive investment and was not just about releasing a few products. However, NVIDIA was up for the challenge and embarked on this new journey, which could open up newer and more expansive markets for them in the future.
NVIDIA's Commitment to Building CUDA
Jensen Huang, CEO of NVIDIA, committed the company to a big undertaking of building CUDA, a platform for scientific computing which they had poured a lot of resources into. Despite not having a market to justify the time and cost, Jensen saw a path to durable differentiation and to own the platform, similar to Apple. He believed that if they don't build it, they can't even possibly have anyone come. Wall Street ignored this until AMD acquired ATI for a lot of money and put in a lot of weight behind it. NVIDIA was threatened as ATI was the only standing legit competitor throughout its whole life.
NVIDIA's Revolutionary CUDA Programming Model for GPU Computation
NVIDIA's CUDA is a revolutionary programming model and platform for GPU computation that enables high-level application development across hundreds of industries, from gaming and design to life and earth sciences, quantum computing, AI, cybersecurity, 5G, and robotics. It took years of investment in compiler teams, SDKs, libraries, developer outreach, and marketing to create a full-stack ecosystem that optimizes across the GPU, acceleration libraries, systems, and applications continuously, iterating between 150+ SDKs and expanding into new application domains. CUDA is a brand new programming model that requires a paradigm shift towards parallel programming and architecture, with over 10,000 cores on their most recent consumer graphics card, making it one of the boldest, iPhone-sized bets that can create a generational company and impact hundreds of industries.
The Significance of CUDA in Parallel Computing
CUDA is a parallel computing platform and programming model invented by NVIDIA. It is designed for parallel execution from the beginning and is used for embarrassingly parallel applications where computations are independent and don't depend on the previous results. While CUDA is entirely free, it's closed source and proprietary software exclusively to NVIDIA's hardware. Nevertheless, it's a perfect example of the Apple business model of giving away an amazing platform ecosystem to developers and making money by selling hardware with healthy gross margins. In 2008, NVIDIA launched the Tegra chip in the market, which was a system on a chip for smartphones, competing with Qualcomm and Samsung. Though it was not a massive success and didn't save the company, it was a significant step towards commercialism.
NVIDIA's mixed success in the tech market
NVIDIA's Tegra processor was widely used in Microsoft Zune HD Media Player and original Tesla Model S touchscreen, which helped the company enter the automotive market, and the processor still powers Nintendo Switch. However, the company failed to succeed in the mobile GPU market despite acquiring mobile baseband company, Icera, in 2011. Meanwhile, Qualcomm acquired Icera's IP and built the Adreno processors, which is its mobile GPU division now. The Android value chain ecosystem has no profit for partners, and Google keeps it that way, which led to NVIDIA's struggles in the Android market. NVIDIA's remarkable growth after 2011 was largely a result of a miracle and perhaps not the best strategy case study.
The Big Bang Moment: How NVIDIA's GPUs Changed the Game for AI and Deep Learning
The big bang moment for AI and NVIDIA was the implementation of AlexNet on GPUs in CUDA, making the deep neural network computationally practical for the first time, and revolutionizing the world of technology, computer science and business. Fei-Fei Li's creation of ImageNet, which spurred the annual algorithm competition, contributed to AlexNet's development. This breakthrough was so significant that NVIDIA's GPUs played a major role in the development of AI and deep learning. The use of parallel computing in GPUs was vital in making it possible, and was the moment that helped usher in the era of computing as we know it today.
NVIDIA's cuDNN Library and the Revolution of Deep Learning
Deep learning has revolutionized the tech industry, enabling computers to recognize images, drive cars, and predict user preferences. NVIDIA's cuDNN library made it easy for data and research scientists to write high performance deep neural networks on NVIDIA hardware, leading to a multi-trillion dollar market in digital advertising. This value accrues to those who help users navigate an explosion of content. While companies like Google and Facebook are on the consumer side, they're all dependent on NVIDIA for chips and software. This paradigm shift has led to NVIDIA being a highly sought-after investment option, similar to investing in Microsoft for Windows or Apple for the iPhone.
NVIDIA's Vision: GPUs and Deep Learning
NVIDIA's success in the stock market was a result of their early recognition of the potential of GPUs to enable deep learning. Although it took years for their vision to materialize, Jensen's faith in the power of GPUs for matrix multiplication paid off when deep learning algorithms turned out to be GPU-friendly. This happy accident formed the foundation of NVIDIA's dominance in the AI chip market, which continues to expand into new applications like robotics and autonomous vehicles. The lesson here is that sometimes successful investments require a long-term vision and faith in an emerging technology, even when the immediate returns aren't obvious. NVIDIA's success is a testament to the power of staying ahead of the curve and taking the risks necessary to make a big impact.
How NVIDIA's Graphics Cards Went from Cryptomining to AI
NVIDIA's graphics cards became popular for cryptocurrency mining in 2016 and 2017 because of their ability to parallelize guess and check processes. When the crypto winter hit in 2018, demand for mining rigs fell off and NVIDIA's revenue declined. However, the same graphics cards were uniquely good at matrix math, making them useful for AI as well. Despite skepticism about deep learning, enterprise use cases for GPUs took off and NVIDIA's data center segment became synonymous with their ML segment. This shows how hardware built for one purpose can have unexpected applications and underscores the importance of understanding demand and use cases for products.
NVIDIA's Data Center Segment Thrives with Machine Learning Hardware
NVIDIA's data center segment is booming as more companies rely on machine learning hardware in their data centers. This has led to NVIDIA's data center revenue segment 3X'ing in the last two years, now generating over $10.5 billion a year in revenue. The prices for the high-end graphics cards sold to data centers are much higher than the prices for consumer cards, with the latest H100 card costing around $20,000 to $30,000 per card. Mellanox, an Israeli data center compute company, was acquired by NVIDIA for $7 billion in 2020 to enable super high bandwidth, super low latency connectivity in the data center between NVIDIA's hardware. The enterprise is not buying cards, but rather buying solutions from NVIDIA that lead to Apple level gross margins
NVIDIA's DPU Revolutionizes Data Centers
NVIDIA has introduced a data processing unit (DPU) which came out of Mellanox acquisition, to transform and communicate data within data centers at a high abstraction layer. It revolutionizes how we think about data centers, making it more efficient and easy to handle how things move around the data center. This addition of DPU acts as a third leg of the stool of computing for NVIDIA, in addition to CPU and GPU. Enterprise and data centers can use it to write at a high abstraction level, letting NVIDIA take care of moving things around the data center. It's the Acquired of insurance to cover employment-related claims and protects companies whether the claim has merit or not.
NVIDIA's Acquisition of Arm and Its Future Plans in the Data Center Market
NVIDIA acquired Arm mainly to expand its presence in the data center market, as Arm CPUs have the potential to be a crucial component in this space. Although Arm's business is simple and based on licensing instruction sets and chip designs, NVIDIA's strategy was to offer Arm licenses to other companies while emphasizing its commitment to keeping the licensing open to all. While regulatory pressure halted the acquisition, NVIDIA continues to pursue its vision of providing its services to Arm-designed IP. NVIDIA is targeting a trillion-dollar market by serving customers in the data center, gaming, and artificial intelligence industries and plans to capture 1% of the $100 trillion opportunity through product innovation and expansion.
NVIDIA's profitable business model and plans for incremental sales through software licensing
NVIDIA is a highly profitable company with a differentiated business model that includes selling software and services bundled with its hardware. Its 66% gross margin business makes it richer in value than FAANG companies. NVIDIA is not speculative, but a cash-generative business that generates $8 billion in free cash flow every year. The company is considering licensing software separately, which will be incremental sales on top of what they already do, as enterprises prefer simple solutions. NVIDIA is also targeting different segments, ensuring there's no cannibalization of hardware customers. NVIDIA's strategy reflects a classic vertical versus horizontal problem that can be resolved using good segmentation. Tim Cook, CEO of Apple, has also focused on selling services as a growing business line. But NVIDIA CEO Jensens is different in his approach, wearing a leather jacket while selling services.
NVIDIA's Revolutionary Technology and Strategic Business Decisions Lead to Continuous Growth and Success in the Gaming Industry.
NVIDIA is growing at 60% a year and has revolutionized the gaming industry with the development of DLSS, a technology that uses deep learning to enhance graphics. They've also capitalized on the crypto boom by releasing dedicated crypto mining cards. Having 60% growth in their DCF model will get them a lot more multiples than companies like Apple or Microsoft, which are growing at slower rates. NVIDIA's add-in board partners like ASUS and MSI sell the majority of their cards to consumers, contributing to their success. Overall, NVIDIA's innovative technology and strategic business decisions have led to their continuous growth and dominance in the gaming industry.
NVIDIA's Hyperion Drive Platform Challenges the Traditional Automakers
NVIDIA is pivoting towards electric vehicles (EVs) and autonomous driving, creating a differentiated experience in a stagnant market. The new Hyperion drive platform will offer full EV, AV hardware, and software stacks, allowing car companies to integrate it into their products without needing to develop their own technology. This shift to EVs and autonomous vehicles challenges traditional automakers, who have struggled to create a meaningfully differentiated experience. NVIDIA's Omniverse creates realistic simulations of important environments for enterprise customers. If successful in the EV and AV market, NVIDIA could control a significant portion of the industry value chain instead of car manufacturers. This would enable NVIDIA to sell their products instead of marketing them through partnerships like Lotus and Amazon.
NVIDIA's Omniverse: A Solution for Simulating and Testing Changes to Real-World Assets
The Omniverse pitch by NVIDIA is an enterprise solution to model any changes to real-world assets before deploying them, helping to avoid testing in production with real-world assets. This approach will save time and resources as it allows efficient running of simulations with a good user interface, targeting to simulate applications with no human intervention. Also, other competitors like Cerberus are focusing on hyper-specialized hardware for enterprises that need specific AI workloads, which could potentially become a significant market in the future. However, it is yet to be seen if these new solutions can compete with NVIDIA and bring a significant shift in the market.
NVIDIA's Competitive Advantage and Industry Positioning
NVIDIA's sustainable competitive advantage is rooted in scale economies, switching costs, and branding. Its CUDA investment enables amortization of 1000+ employees' spend over the 3 million developers and all those who are buying the hardware to use what those developers create. Switching out NVIDIA would boot out CUDA, which creates significant switching costs. Although their process power advantage has gone away, TSMC is still a great partner to manufacture NVIDIA's Ampere series of chips. NVIDIA's competitors, including Google, Cerberus, and AMD, can't justify the CapEx that NVIDIA has to create and maintain their advantage. NVIDIA is a horizontal player who serves every customer. Google, on the other hand, is a vertical player who is constrained by the amount of people using Google Cloud.
Invest in Suppliers of Tools and Infrastructure Rather Than Individual Startups for Long-term Profitability
Investing in the supplier of tools and infrastructure is often a smarter move than investing in individual startups. NVIDIA's success in the AI and deep learning market is attributed to their tight coupling of hardware and software, which highlights the value of owning trade secrets. The complexity of machine learning and semiconductor technology is immense, as demonstrated by the floor planning exercise that involves laying out circuitry and wires on a chip measuring less than the size of a palm. NVIDIA's expansion of mission to accelerate any computing workload reflects their adaptability to market trends and persistent confidence in their strategy despite facing numerous challenges, which is a key attribute of successful companies.
Investing in NVIDIA and Softbank Latin America Fund for Long-Term Success
NVIDIA is a capital-efficient hardware company with 37% operating margins, cash-generative business, and impressive growth potential. Their fabless business model and software and IP-based approach provide them with a distinct advantage, allowing them to spend only a fraction of the capital expenditures as compared to other FAANG-type companies. Though there are challenges with regards to their current share price, their potential for growth and expansion make them an exciting investment opportunity. The Softbank Latin America Fund is another promising option, with the potential for inclusion and modernization across various industries in Latin America. Companies like VTEX are already demonstrating strong growth and promising results. Overall, investing in technology and innovation can be an excellent pathway for long-term success and prosperity.
NVIDIA's Dominance in the Data Center Market and the Importance of Real-World AI Use Cases
NVIDIA's dominance in the data center market and focus on selling solutions through innovation and acquisition, such as with Mellanox, positions them to continue growing. However, belief in real-world AI use cases like autonomous vehicles, Omniverse, and robotics is crucial for sustained growth. Betting against the potential for these physical experiences to intertwine with digital ones is akin to betting against the internet's continued growth. It is hard to imagine a failure case for NVIDIA's business in short order, but a potential pandemic pull forward effect on growth should be considered. Continued innovation and adaptation to redefine GPU as a heftier solution for various compute workloads and hardware integration remains key to remaining a dominant player in the market.
Exploring the Expanse and Seattle with Sony RX100 and Acquired
The Memories Legion compendium of short stories provides great backstory to the Expanse series and fills in gaps for readers. Having a point and shoot camera like Sony RX100 with a long zoom lens is convenient for capturing real camera-style images instead of relying on a phone's camera. The camera also provides great portability during travels. Come to Seattle on May 4th and join Acquired in some fun surprises and after parties. Acquired also has a job board acquired.fm/jobs, so check it out and discover your dream job.