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Acquired
Nvidia Part II: The Machine Learning Company (2006-2022)
Apr 20, 2022
11:11:02
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Quick takeaways
- NVIDIA's breakthrough in deep learning with GPUs and CUDA platform popularized artificial intelligence in various fields.
- Jensen Huang's commitment to GPUs and CUDA platform drove NVIDIA's success in the AI industry.
- NVIDIA's hardware and CUDA platform have enabled advancements in autonomous driving, medical image analysis, natural language processing, and multimedia content creation.
- NVIDIA's market dominance, extensive software ecosystem, and large customer base give them a significant advantage in the AI and machine learning space.
- NVIDIA's expansion into accelerated computing has positioned them as a leading provider of solutions for optimizing computing workloads in various industries.
Deep dives
The breakthrough of deep learning and neural networks
In 2012, a team from the University of Toronto developed the AlexNet algorithm which won a competition in image classification by a significant margin. This breakthrough, built on NVIDIA's GPUs and CUDA platform, popularized deep learning and heralded a new era of artificial intelligence. This development paved the way for advancements in various fields, including self-driving cars, image recognition, natural language processing, and more.
The monumental shift in market perception and stock price
While NVIDIA's stock struggled in the years following the 2008 financial crisis and faced skepticism about its vision for general purpose computing on GPUs, the breakthrough in deep learning in 2012 propelled the company to new heights. It took several years for the market to recognize NVIDIA's pivotal role in artificial intelligence and the value it provided to companies in image recognition, autonomous driving, advertising, and other applications. The stock price subsequently skyrocketed, reaching over $350 per share in recent years.
The visionary leadership of Jensen Huang
Jensen Huang, co-founder and CEO of NVIDIA, played a crucial role in driving the company's focus on GPUs and their potential for deep learning. Despite initial skepticism and challenges, Huang remained committed to his vision and invested heavily in CUDA, the platform that enables developers to utilize the power of NVIDIA's GPUs. His foresight and belief in the importance of parallel processing and specialized hardware have proven to be game-changers in the field of AI and have cemented NVIDIA's position as a leader in the industry.
The broad application and significant market potential
The impact of NVIDIA's breakthrough in deep learning extends far beyond traditional gaming applications. The technology has found application in various fields, such as autonomous vehicles, medical image analysis, natural language processing, and multimedia content creation. The market potential for AI-driven solutions is vast, with companies relying on NVIDIA's hardware and CUDA platform to accelerate their AI algorithms and explore new frontiers in technology and innovation.
The Importance of Scale and Market Dominance
NVIDIA's strong position in the market, with a large customer base, developer community, and extensive software ecosystem, gives them a significant advantage in the AI and machine learning space. They have been able to build on their scale and market dominance to drive growth and maintain pricing power. Their investments in CUDA and hardware acceleration have solidified their position as a trusted provider of graphics processing units (GPUs) for a wide range of applications beyond gaming.
Expansion of Mission and Strategy
NVIDIA has successfully expanded its mission from enabling graphics as a storytelling medium to becoming a provider of accelerated computing solutions for a wide range of workloads. By embracing the concept of accelerated computing, NVIDIA has been able to leverage its hardware, software, and services to serve enterprises across various industries. This expansion has allowed them to tap into a trillion-dollar market opportunity and position themselves as a leading provider of solutions that optimize the performance and efficiency of computing workloads.
Capital Efficient Business Model
NVIDIA's capital expenditures (capex) are relatively low compared to other tech giants, such as Apple, Microsoft, and Google. With only a billion dollars spent on capex per year, NVIDIA has been able to generate significant revenue and profitability. Their focus on software and platform development, coupled with strategic partnerships and licensing arrangements, has allowed them to efficiently allocate their resources and compete effectively in the market. This capital efficiency contributes to their strong financial performance and sustainable competitive advantage.
Envidia's Dominance in the Data Center Market
Envidia has been experiencing incredible growth in the data center market, maintaining their dominance with a 75% year-over-year growth rate. Their ability to innovate and redefine the definition of GPUs, incorporating specialized functions and hardware, sets them apart from their competitors. With their recent acquisition of Mellanox, they are poised to become the go-to provider for AI data center hardware, offering a complete package for accelerated computing. Their focus on machine learning and deep learning applications in the digital and physical worlds positions them to capitalize on the enormous potential of autonomous vehicles, robotics, and the Omniverse.
Envidia's Enterprise Solutions and the Omniverse
Envidia's enterprise solutions, such as the Omniverse, offer powerful tools for simulations and modeling. The Omniverse allows enterprises to simulate and model real-world assets before implementing changes, providing an efficient and effective way to test and optimize various scenarios. Envidia's aim is to be the one-stop-shop for AV hardware and software, partnering with car manufacturers to create highly differentiated driving experiences. Additionally, their focus on inclusion and bringing technology to underserved populations in Latin America further showcases their commitment to innovation and growth. Overall, Envidia's ability to capture market share in the digital and physical worlds, along with their high operating margins, positions them for continued success.
49.
The Scale Economy, Switching Costs, Network Economy, Branding and Cornered Resource
2min
50.
TSMC Is Working With These New Start Ups, Billion Dollar Funded Silicon Companies
3min
By 2012, NVIDIA was on a decade-long road to nowhere. Or so most rational observers of the company thought. CEO Jensen Huang was plowing all the cash from the company’s gaming business into building a highly speculative platform with few clear use cases and no obviously large market opportunity. And then... a miracle happened. A miracle that led not only to Nvidia becoming the 8th largest market cap company in the world, but also nearly every internet and technology innovation that’s happened in the decade since. Machines learned how to learn. And they learned it... on Nvidia.
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Sponsors:
Statsig: https://bit.ly/acquiredstatsig24
Vanta: https://bit.ly/acquiredvanta
Crusoe: https://bit.ly/acquiredcrusoe
Links:
- Ben Thompson’s great Stratechery interview with Jensen
- Linus Tech Tips tests an Nvidia A100
- Episode sources
Carve Outs:
- The Expanse short story collection, Memory's Legion
- Sony RX100 point-and-shoot camera
Note: Acquired hosts and guests may hold assets discussed in this episode. This podcast is not investment advice, and is intended for informational and entertainment purposes only. You should do your own research and make your own independent decisions when considering any financial transactions.
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