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September 6, 2023 • 2hr 53min
Nvidia Part III: The Dawn of the AI Era (2022-2023)
Acquired
In this episode, the Acquired hosts dive into the latest chapter of Nvidia's remarkable story - its emergence as the dominant platform powering the AI revolution. Just 18 months after the hosts last covered Nvidia, the company has weathered a massive stock crash, only to roar back and become the first semiconductor company to reach a $1 trillion market capitalization.
Nvidia Part III: The Dawn of the AI Era (2022-2023)
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Key Takeaways
- Nvidia has emerged as the dominant platform powering the AI revolution, particularly the rise of large language models (LLMs) like ChatGPT
- Nvidia's competitive advantages include its CUDA software ecosystem, vertical integration from chips to full data center solutions, and cornered access to critical TSMC manufacturing capacity
- The combination of generative AI's explosive growth, the need for massive GPU compute to train these models, and Nvidia's platform leadership has propelled the company to over $1 trillion in market value
- Nvidia is positioning itself as the "IBM of the AI era" - a full stack provider of hardware, software, and services for the new era of accelerated computing
- Potential risks include competition from cloud providers, the possibility of a generative AI hype cycle, and the challenge of maintaining Nvidia's wide lead as the market grows
Introduction
In this episode, the Acquired hosts dive into the latest chapter of Nvidia's remarkable story - its emergence as the dominant platform powering the AI revolution. Just 18 months after the hosts last covered Nvidia, the company has weathered a massive stock crash, only to roar back and become the first semiconductor company to reach a $1 trillion market capitalization.
The key driver of Nvidia's meteoric rise has been the breakthrough of large language models (LLMs) like OpenAI's ChatGPT, which have captured public imagination and demonstrated the transformative potential of generative AI. Nvidia's hardware and software have become the foundational infrastructure powering the training and deployment of these models, cementing the company's position as the "platform" for the new era of accelerated computing.
Topics Discussed
The "Big Bang" of AI (12:39)
- In 2012, the Alexnet algorithm, developed by researchers at the University of Toronto, marked a major breakthrough in the field of machine learning by using Nvidia GPUs to dramatically outperform previous image recognition models
- This "big bang" moment kicked off a wave of AI research and commercialization, with tech giants like Google and Facebook rapidly acquiring top AI talent and using Nvidia's hardware to power their machine learning-driven products
- However, by the mid-2010s, there were concerns that this AI research was becoming too concentrated at a few large tech companies, leading Elon Musk and Sam Altman to try to create an independent AI research lab in OpenAI
The Rise of Transformers and Large Language Models (32:35)
- In 2017, Google's "Transformer" paper introduced a new machine learning architecture that enabled more efficient and scalable training of language models
- This paved the way for the development of ever-larger language models, culminating in OpenAI's GPT-3 in 2020 and the recent breakthrough of ChatGPT in 2022
- The key innovation was the ability to "pre-train" these models on vast amounts of unlabeled text data, allowing them to learn the underlying structure of language in a more general way
- However, training these massive models requires immense amounts of GPU compute power, playing directly to Nvidia's strengths
Nvidia's Vertical Integration and Platform Strategy (1:42:57)
Nvidia's Cornered Resource: TSMC Capacity (2:09:17)
- Nvidia's ability to reserve large amounts of TSMC's most advanced chip manufacturing capacity, particularly for its 2.5D "chip-on-wafer-on-substrate" (CoWoS) packaging, gives it a critical competitive advantage
- This cornered resource is extremely difficult for competitors to replicate, as it requires massive upfront investments, specialized expertise, and years of lead time to build out new fabs
- Nvidia's early and sustained investments in this area have locked in its position as the premier provider of GPU hardware for AI workloads
Nvidia's Financials and Margins (1:47:39)
- Nvidia's gross margins have climbed from 24% pre-CUDA to over 70% today, as it has built a highly differentiated platform
- The company's recent quarterly results, with data center revenue more than doubling year-over-year, demonstrate the explosive demand for its AI-optimized hardware and services
- Nvidia is able to command premium pricing and margins due to the mission-critical nature of its technology for the AI revolution, as well as its strong brand and network effects
Competing with Nvidia (2:46:06)
- Competing with Nvidia head-on would require replicating its entire vertical stack - from custom chip architectures to software ecosystems to global manufacturing partnerships
- The hosts argue this is effectively impossible for any single competitor, given the decades of investment and scale Nvidia has built
- Potential avenues for competition may come from flanking attacks, such as cloud providers leveraging open-source frameworks like PyTorch, or breakthroughs in more efficient AI model architectures
- However, Nvidia's strong position, fast execution, and ability to continuously innovate make it extremely difficult to unseat as the leading platform for accelerated computing
Conclusion
The Acquired hosts conclude that Nvidia has positioned itself as the essential platform for the new era of AI and accelerated computing. Through a combination of technical innovation, vertical integration, and cornered access to critical manufacturing resources, Nvidia has built a nearly impregnable moat around its business.
While there are potential bear cases around competition, hype cycles, and the evolution of AI workloads, the hosts believe Nvidia's strengths as a platform company, its relentless execution, and the massive scale of the opportunity ahead make it extremely well-positioned to maintain its dominance. Nvidia's journey from a graphics card company to the "IBM of the AI era" stands as a remarkable example of a technology company leveraging its core competencies to ride the waves of industry transformation.
