Podcast Summary
Revolutionizing industries with powerful GPU architecture: NVIDIA's technology enables simulations of the physical world, from airflow to drug discovery, requiring unfathomable data and compute, with Statsig processing over 130B events daily and serving experiments to 1.2B end users.
NVIDIA is revolutionizing industries from gaming to enterprise data centers to scientific computing by providing the hardware, software, and services to simulate the physical world with their powerful GPU architecture. This is no longer just for gamers, but is making it possible to predict airflow over a wing, simulate cell interaction for drug discovery, and even model climate change. The scale of data and compute required for these advancements is unfathomable, with one speech recognition model requiring more math operations than the number of grains of sand on Earth. Meanwhile, Statsig, a company we've previously featured on Acquired, is processing over 130 billion events per day and serving experiments to over 1.2 billion end users. This infrastructure supports an incredible amount of data and statistical jobs, and Statsig has added major players in the AI industry to their customer base. These advancements highlight the incredible potential and scale of technology companies today.
NVIDIA's success driven by relentless innovation and user experience control: NVIDIA's early innovation, producing graphics cards with faster ship cycles and writing their own drivers, set the foundation for their long-term success in the tech industry by catering to the high-end gaming market's demand for superior user experience
NVIDIA's success in the tech industry can be attributed to their relentless innovation and control over their user experience. Starting from their early days, they managed to survive in a crowded market by producing graphics cards with impressive 6-month ship cycles, much faster than their competitors. Additionally, they made a bold move by writing their own drivers for their graphics cards, ensuring a superior user experience and building a team of low-level software developers. This decision came with a high fixed cost but provided long-term benefits. Furthermore, their innovation extended to programmable shaders, which they introduced before their competitors. All these advancements were focused on catering to the high-end gaming market, where users demanded the best experience and were willing to invest in top-tier graphics cards. The company's commitment to controlling as much of the user experience as possible set the foundation for NVIDIA's continued success in the tech industry.
Expanding NVIDIA's Reach Beyond Gaming: NVIDIA's founder, Jensen, saw potential in expanding beyond gaming and recognized the need to provide developers with the whole abstraction stack to make GPUs accessible for general-purpose computing. This led to the creation of various SDKs and frameworks.
NVIDIA's success in the gaming market led its founder, Jensen, to look beyond and envision the company's potential in serving other domains. The relationship between NVIDIA and developers was strengthened, offering advantages for those who developed for their specific hardware. However, to make GPUs accessible for general-purpose computing, a massive undertaking was required. NVIDIA needed to provide the whole abstraction stack on top of the programming language, similar to what Apple offers to iOS and Mac developers. This involved creating various SDKs and frameworks. Jensen recognized the magnitude of this endeavor but was determined to pursue it, aiming to expand NVIDIA beyond a gaming company. The story of the quantum chemistry researcher using NVIDIA's graphics cards for non-graphical problems highlighted the need for such an initiative and the potential benefits it could bring.
NVIDIA's Long-Term Vision for CUDA Platform: NVIDIA invested in CUDA platform despite initial market uncertainty, driven by passion and business model, setting the stage for future success.
NVIDIA's commitment to developing its own CUDA platform represents a significant investment and market undertaking. While the company has the necessary low-level programming talent to start this project, investors would rightfully ask about the business case and market potential. The market for scientific computing, particularly in research domains, is the initial focus, but the total addressable market may not justify the costs and resources required. NVIDIA's CEO, Jensen Huang, is driven by both a passion for the market and a business model realization, aiming to own the platform and build relationships with developers for differentiation. Despite initial market uncertainty, Jensen's belief is that "if you don't build it, they can't come," indicating a long-term vision that Wall Street may not have fully understood at the time. The acquisition of ATI by AMD in 2007 introduced a significant competitor, but NVIDIA continued to invest in CUDA, setting the stage for its future success.
NVIDIA's Bet on CUDA: From Gaming to Leading Computing Platform: NVIDIA's acquisition and development of CUDA transformed the company from a gaming-focused graphics processor manufacturer to a leading computing platform provider in various industries
NVIDIA's acquisition of CUDA was a bold bet that paid off, transforming the company from a gaming-focused graphics processor manufacturer to a leading computing platform provider. At a time when NVIDIA's stock dropped 80% during the financial crisis, Jensen Huang, the CEO, didn't give up. Instead, he focused on building CUDA, a full development framework for computation on GPUs, which was a game-changer. Initially, CUDA was a programming language and computing platform, but it evolved into an API, an extension of C or C++, and a high-level application development framework. NVIDIA optimized the full stack, iterating between GPUs, acceleration libraries, systems, and applications, and expanded the reach of the platform to various industries. This required creating a compiler team, thinking about SDKs, and libraries from scratch. Today, CUDA is used in industries ranging from gaming and design to life and earth sciences, quantum computing, AI, cybersecurity, 5G, and robotics. CUDA's success is a testament to Jensen's vision and NVIDIA's commitment to innovation.
Nvidia's Revolutionary Parallel Computing Platform CUDA: Nvidia's CUDA platform transformed programming by enabling parallel computing on GPUs, attracting developers with free access and justifying investment through market growth
Nvidia revolutionized the programming industry with CUDA, a parallel computing platform designed for the unique architecture of GPUs. This was a shift from traditional serial execution of programs to parallel programming, which was embarrassingly parallel due to the massive number of cores and independent computations. Nvidia made CUDA available for free, attracting developers to build software on their hardware. However, this model was closed source and proprietary to Nvidia's GPUs, making it similar to Apple's business model but also distinct due to the full integration with the hardware. Despite initial skepticism, Nvidia's investment in CUDA proved successful as the market for GPUs grew, justifying the investment and enabling the scale required for this business model.
NVIDIA's Mobile Detour: NVIDIA's foray into mobile technology with Tegra chip and platform didn't save the company but led to opportunities in automotive and gaming industries
NVIDIA's journey to success was not a straight line, and their foray into mobile technology with the Tegra chip and platform was a significant departure from their core competencies. Although it didn't save the company, it did lead to opportunities in automotive and gaming industries, as the Tegra chip powered the touchscreen infotainment system in the Tesla Model S and is still the main processor of the Nintendo Switch. However, NVIDIA faced challenges in the mobile market due to the lack of profits and Google's control over the ecosystem. Despite these setbacks, NVIDIA continued to invest in mobile technology by acquiring a mobile baseband company called Icera in 2011, but later shut down the acquisition and laid off the founders, who went on to create Graphcore, a potential competitor to NVIDIA. Overall, NVIDIA's experience in mobile technology was a learning process that led to new opportunities and challenges.
NVIDIA's Fortunes Changed with the Emergence of Deep Learning: Being in the right place at the right time and having the right technology can lead to significant growth for a company in emerging tech trends
The success of NVIDIA in the tech industry can be compared to a whale in a large ocean, with other significant companies co-existing. This analogy is drawn from NVIDIA's history of ups and downs in the early 2000s and 2010s, where they experienced periods of growth and stagnation. However, their fortunes changed in 2011 when a "miracle" occurred in the form of ImageNet, a database of labeled images used for artificial intelligence research. This project, led by Fei-Fei Li, a Princeton computer science professor, led to the creation of the ImageNet competition, which brought great renown to Li and her team. In the 2012 competition, a team from the University of Toronto, led by Alex Krizhevsky, submitted an algorithm called AlexNet, which was a convolutional neural network and a breakthrough in the field of deep learning. This approach was significantly more accurate than previous methods, and it required immense computational power, which NVIDIA provided through their graphics processing units (GPUs). Thus, NVIDIA's fortunes were revived, and they became the go-to company for deep learning and artificial intelligence applications. This story illustrates the importance of being in the right place at the right time and having the right technology to capitalize on emerging trends.
A breakthrough in computer science and technology: The combination of Moore's Law and NVIDIA's parallel architecture enabled deep learning, leading to advancements in image recognition, self-driving cars, gaming, and digital advertising, collectively worth trillions, and enabling machines to perform human-like tasks.
The development of CUDA and the implementation of deep learning algorithms on NVIDIA GPUs marked a pivotal moment in computer science, technology, business, and our daily lives. Prior to this, training deep neural networks required an impractical amount of compute, making it a distant future prospect. However, the combination of Moore's Law and NVIDIA's massively parallelizable architecture made it possible. This breakthrough led to advancements in various fields, including image recognition, self-driving cars, gaming, and digital advertising, which collectively forms a multi-trillion dollar market. The importance of this moment cannot be overstated, as it enabled machines to perform tasks previously thought to be exclusive to humans. The ease and efficiency of deep learning have led to exponential growth in the field, with transformer models now capable of handling millions or even billions of parameters. This technological advancement has opened up new possibilities and has the potential to revolutionize industries and our daily lives.
Navigating the era of digital content with essential tools: NVIDIA's success highlights the value of investing in emerging technologies and visionary leaders, despite initial skepticism.
The value of technology companies in the era of abundant digital content accrues to those who help navigate the content and provide essential tools for effective navigation. A prime example of this is NVIDIA, a company that has seen significant growth due to its role in enabling deep learning technology. Despite clear signs of this shift starting in the late 2010s, many investors and analysts missed the opportunity to invest in NVIDIA's stock during this time, as its value remained low. This was largely due to skepticism towards Jensen Huang's vision of the future of neural networks and the role of GPUs in computing. However, NVIDIA's persistence in building the necessary hardware and software paid off, and the company's market cap reached $20 billion once again in 2016, a decade after it had first achieved this milestone. This serves as a reminder of the importance of recognizing and investing in emerging technologies and the visionary leaders who drive their development.
NVIDIA's GPU success from deep learning and crypto mining: NVIDIA's GPU success came from deep learning and crypto mining due to their parallel nature, making them more effective than CPUs for these applications. This led to advancements in robotics, autonomous vehicles, and other fields.
The parallel processing capabilities of graphics processing units (GPUs) accidently aligned with the requirements of deep learning and cryptocurrency mining, leading to a significant boost in revenue for NVIDIA. Jensen Huang, NVIDIA's CEO, had a vision for the future and kept a close eye on emerging applications for GPUs. When deep learning and cryptocurrency mining gained prominence, NVIDIA was quick to capitalize on the opportunity. The massive parallelism in both deep learning and cryptocurrency mining allowed GPUs to excel, making them much more effective than CPUs for these applications. The success of GPUs in deep learning paved the way for advancements in robotics, autonomous vehicles, and other fields. Similarly, the parallel nature of cryptocurrency mining made GPUs an ideal choice for this application during the crypto boom. Despite the existence of more optimized solutions for AI and crypto mining, the general-purpose GPUs offered by NVIDIA and its competitors proved to be good enough for these use cases. This happy accident of history underscores the importance of being attentive to emerging trends and the versatility of GPUs in handling complex, parallel computations.
NVIDIA's Data Center Revenue Surge Amidst Crypto Winter: NVIDIA's data center revenue surged during the crypto winter due to enterprise use cases in deep learning, generating around 3 billion dollars, while gaming segment remained the revenue leader with nearly 6 billion dollars.
The accelerated computing market has evolved, with the focus shifting from CPU to GPU acceleration for various workloads. NVIDIA, a key player in this space, experienced a significant challenge during the crypto winter, where the demand for their GPUs for crypto mining plummeted. Due to poor visibility into the end use of their products, NVIDIA faced criticism from the market. However, the enterprise use case for GPUs in deep learning took off, leading to a surge in data center revenue, which became synonymous with NVIDIA's ML segment in my mind. The prices for data center hardware are significantly higher than consumer-grade GPUs, making it a lucrative market. Tesla, a major customer, is now making their own chips for inference and training, but still relies on NVIDIA for the current compute cluster. In 2020, the data center segment, which was previously half the size of the gaming segment, generated around 3 billion dollars in revenue, but the gaming segment remained the revenue king with nearly 6 billion dollars.
NVIDIA's Data Center Segment Surpasses $10.5 Billion in Revenue: NVIDIA's data center business, fueled by machine learning hardware demand, reached $10.5B in revenue, comparable to gaming segment. NVIDIA's acquisition of Mellanox & prohibition of consumer cards led to high-margin Data Processing Units (DPUs).
NVIDIA's data center segment has experienced exponential growth in the last two years, reaching over $10.5 billion in revenue, making it comparable in size to the gaming segment. This growth is due to the increasing demand from enterprises for machine learning hardware, which NVIDIA is selling at high gross margins. In 2018, NVIDIA changed the user agreements of their consumer cards to prohibit their use in data centers, and in 2020, they acquired Mellanox, an Israeli data center compute company, to improve connectivity and communication between hardware in the data center. This acquisition has enabled NVIDIA to introduce a new component, the Data Processing Unit (DPU), to their offerings, completing their "stool of computing" with the CPU, GPU, and DPU. The data center is now the "black box," and NVIDIA handles data movement within it at a high abstraction level. Vanta, a leading trust management platform, automates security reviews and compliance efforts, making it an excellent example of the efficient and simplified solutions that NVIDIA provides for data centers.
Focus on core value, outsource the rest: Companies should concentrate on their unique strengths and outsource tasks like compliance and security to simplify processes and save time.
Companies should focus on what truly adds value to their product and outsource the rest, including compliance and security. Vanta, a software solution, simplifies this process by automating compliance and security reviews, saving organizations countless hours of work. This allows companies to return their focus to their core mission, like NVIDIA's ambition to own the data center and create ARM-based data center CPUs. However, NVIDIA's attempt to acquire ARM faced regulatory pressure due to Arm's business model, which relies on licensing its instruction set and chip designs to various companies. This meant that if NVIDIA were to acquire Arm, there could be potential conflicts of interest. Ultimately, NVIDIA abandoned the acquisition, but the lesson remains: focus on what makes your product better and outsource the rest.
Nvidia's Expansion Beyond Hardware: Targeting a $1T Market: Nvidia, valued at $500B, plans to expand beyond hardware with CUDA for ARM chips, next-gen GPUs, CPUs, and Omniverse platform, aiming for a $1T market share.
Nvidia, a company valued at around half a trillion dollars, is aggressively expanding its market reach beyond its traditional hardware business. Historically, Nvidia's strategy has been centered around CUDA and its ecosystem, which caters exclusively to Nvidia's hardware. However, the company has expressed interest in making this technology available for ARM chips, aiming to capture a larger share of the $1,000,000,000,000 market they've identified. Nvidia's ambition is evident in their recent announcements, such as the next-generation GPU architecture, Hopper, and the Grace CPU. These advancements, along with their Omniverse platform, were showcased at their annual GPU developer conference, GTC. During their investor day presentation, CEO Jensen Huang emphasized their plans to target a $1,000,000,000,000 market, aiming to capture just 1% of it. Despite the significant valuation, Nvidia's financials are impressive, generating $27,000,000,000 in revenue last year and holding a 66% gross margin. Their high valuation is justified, as they have a substantial competitive advantage and generate substantial free cash flow. The company's ambition, financial strength, and technological innovation make it a generational company worth admiring.
NVIDIA's Licensing Strategy: Expanding Beyond Hardware: NVIDIA's licensing strategy generates incremental revenue and avoids hardware sales cannibalization by catering to enterprise segment with proprietary software solutions, like CUDA, through segmentation, pricing, distribution, and terms of service.
NVIDIA is expanding its business model by licensing industry-defining software separately from its hardware, such as CUDA and other industry-leading technologies. This strategy allows the enterprise segment to access proprietary software solutions, which they cannot obtain through open-source platforms. This not only creates an incremental revenue stream but also avoids potential cannibalization of hardware sales. NVIDIA's approach to licensing is based on segmentation and careful pricing, distribution, and terms of service, ensuring that it caters to a distinct market. With a workforce of around 20,000 employees and $27 billion in revenue last year, NVIDIA's growth rate of 60% is remarkable, making it a formidable player in the tech industry. The company's focus on gaming, with advancements like ray tracing and DLSS, further highlights its innovation and commitment to delivering cutting-edge technology.
DLSS technology revolutionizes gaming with lower resolution rendering and deep learning upscaling: NVIDIA's DLSS technology enhances gaming visuals and frame rates by rendering at lower resolutions and using deep learning to upscale, while also collaborating with game developers and addressing crypto mining concerns.
NVIDIA's deep learning super sampling (DLSS) technology is revolutionizing the gaming industry by rendering games at a lower resolution and then using deep learning to upscale the output for higher resolution and frame rate. This innovation allows for phenomenal visuals and full frame rate even in high-resolution games. NVIDIA's strategy involves collaborating closely with game developers to optimize their games for the latest hardware. Additionally, NVIDIA has addressed the crypto mining issue by creating dedicated crypto mining cards and limiting the mining capabilities on their gaming cards. Lastly, NVIDIA's partnership with add-in board partners allows them to maintain control and profitability while allowing third parties to handle branding and cooling. Despite the control NVIDIA has over its business, it's likely that they will shift towards more direct sales as they continue to grow. Another segment NVIDIA is focusing on is automotive, which has been a small revenue contributor but has potential for significant growth.
NVIDIA shifting from commodity components to creating differentiated experiences in EV and autonomous driving market: NVIDIA aims to provide full hardware-software stack for EV and autonomous driving market, potentially controlling larger portion of value chain, and introduces Omniverse, a platform for sophisticated simulations for enterprises, revolutionizing testing and production.
NVIDIA is making a strategic shift from supplying commodity components to automakers towards creating a differentiated experience in the electric vehicle and autonomous driving market. The company aims to provide a full hardware-software stack, leaving the metal and glass components to car manufacturers. This could potentially lead to NVIDIA owning a larger portion of the value chain in the automotive industry, as opposed to auto manufacturers controlling the experience forever. Another key point is NVIDIA's Omniverse, a platform for creating sophisticated simulations for enterprises, which could become an essential tool for businesses to test and optimize their real-world assets before deployment. This enterprise metaverse is not designed for human interaction but rather for simulating and optimizing real-world assets, potentially revolutionizing the way businesses approach testing and production.
Competition in the Data Center Market for Accelerated Computing: NVIDIA leads in general-purpose GPUs, but faces competition from Cerebras, Graphcore, and potential in-house solutions from big tech companies. Google's TPUs offer cost advantages for certain workloads, and Cerebras' hyper-specialized hardware targets specific AI workloads.
The future of computing, particularly in the data center market, is up for grabs with NVIDIA leading the charge in general-purpose GPUs, but facing competition from companies like Cerebras and Graphcore who are proposing alternative approaches to accelerated computing. The bull case for NVIDIA rests on their ability to capture a small percentage of the massive $100 trillion market opportunity. However, the bear case argues that NVIDIA's competitive advantage may not be sustainable due to the potential for big tech companies to design and produce their own silicon. Additionally, Google's counterpositioning through their TPUs, which may offer cost advantages for certain workloads, adds another layer of competition. Cerebras, with its dinner plate-sized chip and high power consumption, is betting big on hyper-specialized hardware for enterprises doing specific AI workloads. The outcome of this competition will significantly impact the enterprise AI data center market.
NVIDIA's competitive advantages maintain pricing power: NVIDIA's market dominance is due to scale economies, switching costs, network effects, and a vast customer base. Intellectual property protection is crucial to maintain this position.
NVIDIA's significant market position and competitive advantages, including scale economies, switching costs, and network effects, enable the company to maintain pricing power over competitors in the GPU market. With a rich valuation, NVIDIA's continued growth is a key factor in justifying this valuation. While other tech giants like Google, Apple, and Amazon have the capability to develop their GPUs in-house, the enormous investment required to replicate NVIDIA's 15-year CUDA ecosystem and surpass it is a substantial challenge. Additionally, NVIDIA's ability to serve a vast customer base, from individual developers to enterprises, further solidifies its market dominance. The recent cybersecurity incident that led to the theft of NVIDIA's source code highlights the importance of NVIDIA's intellectual property and the potential consequences if it were to be made publicly available.
NVIDIA's vertical integration and ownership of CUDA and driver stack give it significant value and pricing power: NVIDIA's success in the AI market is due to its dominant platform position, similar to Apple and Microsoft, and investing in companies that sell necessary tools and infrastructure during technological gold rushes
NVIDIA's vertical integration of hardware and software, particularly, its ownership of CUDA and driver stack, gives it significant value and pricing power in the AI and deep learning market. This makes NVIDIA a dominant platform in the industry, similar to how Apple and Microsoft are in their respective markets. Another key takeaway is the importance of investing in companies that sell the necessary tools and infrastructure during a technological gold rush. NVIDIA's success in the AI market has been compared to investing in picks and shovels during the gold rush. Lastly, the complexity of creating semiconductors, semiconductor manufacturing, and machine learning is unfathomable. Floor planning, the process of laying out all the circuitry and wires on a chip, involves planning for millions of "rooms" and involves dozens of miles of "wires" on a single GPU, which is mind-boggling when considering the size of the chips. NVIDIA's success can also be attributed to its resilience, as the company has survived multiple challenges and setbacks throughout the years.
NVIDIA's Capital Efficiency and High Operating Margins: NVIDIA's capital efficiency as a fabless semiconductor company leads to high operating margins and significant cash for shareholders. Its partnership with Crusoe and potential expansion into solution sales contribute to its market dominance and bullish investment scenario.
NVIDIA's capital efficiency as a fabless semiconductor company allows it to maintain high operating margins and generate significant cash for shareholders, despite spending less on capital expenditures compared to other tech giants. With a hardware business model and 37% operating margins, NVIDIA outperforms software-focused companies like Apple and even hardware-focused ones like Facebook and Google. Furthermore, NVIDIA's partnership with Crusoe, a clean compute cloud provider, enables lower energy costs and better performance for AI workloads. The a plus case for NVIDIA involves maintaining market dominance in the data center market and continuing to expand gross margins through solution sales. The bullish investment scenario assumes the success of the Mellanox acquisition and the development of a data processing unit, making NVIDIA a one-stop shop for AI hardware.
NVIDIA's Success Depends on AI's Growth in Digital and Physical Worlds: NVIDIA's continued success relies on AI's expansion into physical applications like autonomous vehicles and robotics, with potential growth driven by innovation and integration of new technologies.
NVIDIA's continued success hinges on the growth of AI and its applications in both the digital and physical worlds. The company has already made significant strides in the digital realm, but the real upside could come from the expansion into physical applications like autonomous vehicles and robotics. NVIDIA's ability to innovate and integrate new technologies will be crucial in maintaining its market dominance. Shareholders should believe in the potential of these emerging markets and NVIDIA's role in them, but it's important to consider that some growth may have been pandemic-driven. The failure case could be a slowdown in growth if the physical applications don't materialize as expected. A potential carve-out is the influence of science fiction, specifically the "Expanse" series, on the authors' short stories, which showcase the potential of NVIDIA's technology in imaginative ways.
Exploring the Harry Potter universe through Fantastic Beasts spin-offs and personal experiences with a Sony camera: The Fantastic Beasts films add depth to the Harry Potter universe, while the Sony RX 100 offers a convenient and high-quality photography solution, despite not matching a full-frame mirrorless camera's capabilities.
The Fantastic Beasts spin-off films provide intriguing insights into the Harry Potter universe by shedding light on lesser-known characters and backstories. Meanwhile, in a different context, the speaker shares his experience with using a Sony RX 100 point-and-shoot camera. He appreciates its convenience and the high-quality images it produces, even though it doesn't match the capabilities of a full-frame mirrorless camera. The speaker also discusses his recent purchase of a camera cube for his Sony Alpha 7 C, expressing excitement about the potential for more in-person events. Overall, both the Fantastic Beasts films and the Sony RX 100 camera offer unique value, whether it be through expanding the Harry Potter narrative or providing a compact yet powerful photography solution.
