The Birth of NVIDIA:
NVIDIA was founded in 1993 by Jensen Huang, Chris Malachowski, and Curtis Priam. The trio, all former Sun Microsystems and LSI Logic engineers, aimed to create 3D graphics cards for personal computers. They chose this niche despite market skepticism and competition due to their belief in the potential of PC gaming.

PC Gaming as the Target Market:
NVIDIA recognized the growing popularity of PC gaming and saw an opportunity to provide high-quality graphics for this emerging market. The PC revolution was just beginning, offering a platform that could be used for both work and entertainment. 3D graphics were nonexistent in PCs at the time, presenting a unique opportunity for innovation.

Challenges and Doubts:
The market for 3D graphics chips was crowded with established competitors. The target audience, PC gamers, was not taken seriously as a viable market. Gaming was seen as a frivolous activity and not a serious application.

The Crucible Decision:
NVIDIA faced a crucial decision: enter the competitive chipmaking field and target the unproven market of PC gaming. The trio had to decide whether to bet on the potential growth of the gaming market and position their innovation accordingly.

First Principles and the Start of NVIDIA:
Chris Malachowski emphasizes the importance of starting from first principles and questioning assumptions to drive action. In early 1993, Malachowski joined Jensen Huang and Curtis Priem to start NVIDIA.

Seeking Investors:
The founders sought investors, and Wilfred Corrigan, head of LSI Logic, recommended Don Valentine of Sequoia Capital. Valentine, despite Malachowski’s poor pitch, agreed to invest due to Corrigan’s strong endorsement.

Developing the NV1 Chip:
NVIDIA began operations in a small office and hired around 20 people. They designed and built the NV1 chip, the company’s first product, within 18 to 24 months.

NV1: A Technological Achievement but a Market Failure:
The NV1 chip was a technological marvel with various features, including 3D graphics, video processing, and audio capabilities. However, it was a commercial failure due to its high price, lack of targeted applications, and complexity. The chip was likened to an octopus due to its numerous dongles and connections.

Customer Response and Diamond Multimedia:
Diamond Multimedia, NVIDIA’s customer partner, purchased 250,000 NV1s, but retail sales were poor. Diamond panicked, as the NV1 did not meet market expectations for a low-cost, high-performance 3D graphics chip.

NV1 Product Failure and Lessons Learned:
NVIDIA’s initial product, the NV1, was a commercial failure. The product suffered from poor market fit, pricing issues, and lack of functionality. The failure led to valuable lessons about product-market fit, positioning, and competition analysis.

The Problem with the NV1 Architecture:
NVIDIA’s NV1 and NV2 chips used a quadrangle rendering architecture. Competitors emerged with chips supporting a triangle-based rendering architecture, which became the industry standard. NVIDIA realized they were on the wrong path and faced a difficult decision.

Existential Moment and Difficult Decision:
NVIDIA faced a choice between finishing the Sega game console contract with the wrong architecture or breaking the contract to start fresh. Finishing the contract would ensure revenue but lead to long-term failure. Not finishing the contract meant breaking ties with Sega but gave NVIDIA a chance to survive.

Abandoning the NV2 and Adopting a New Architecture:
NVIDIA decided to abandon the NV2 and switch to the new inverse texture mapping architecture. This decision required scrapping the current project and starting from scratch. It was a risky move, but it was necessary for the company’s long-term success.

Sega’s Unforeseen Kindness:
Chris Malachowski, NVIDIA’s CEO, approached Sega’s CEO, Irimadrisan, to discuss the situation. Malachowski explained that finishing the console would harm both companies. Despite no obligation, Irimadrisan agreed to release NVIDIA from the contract and pay them in full. This act of kindness gave NVIDIA a lifeline and allowed them to continue operations.

Financial Struggles and Concerns:
After the NV1 failure and the abandonment of the NV2, investors were concerned about NVIDIA’s future. The company was burning cash and faced fierce competition for talent. NVIDIA’s survival was in question, and they were constantly facing the threat of going out of business.

One Last Attempt:
With limited resources, NVIDIA made one final attempt to create a breakthrough chip. The outcome of this effort would determine the fate of the company.

One-Shot Opportunity:
NVIDIA faced a critical challenge in developing a perfect chip in one shot due to the time-consuming traditional process of chip iteration.

Emulation as a Solution:
Jensen Huang, NVIDIA’s CEO, emphasized the importance of working backward to ensure a perfect chip on the first try. Chris Malachowski proposed doing all the software work in advance to minimize the need for multiple tape-outs.

Unexpected Acquisition:
Malachowski discovered that Icos, a company that had gone out of business, had developed a system emulator called Icos. Despite Icos being out of business, Malachowski managed to acquire one of their emulators, which was crucial for their chip development.

Emulating the First PC Chip:
NVIDIA used the Icos emulator to emulate Revo 120 MV3, the world’s first PC chip. This emulation allowed them to test and verify the chip’s functionality before tape-out.

Success and Revolution:
The chip worked perfectly on the first tape-out, showcasing the genius of NVIDIA’s engineers. The chip’s revolutionary inverse texture mapping engine surpassed competitors and transformed modern computer graphics.

Founding and Early Struggles:
NVIDIA was founded in 1993 and faced challenges in developing its first graphics chips. The company’s Reaver 128 chip in 1995 was a success, selling one million units in four months and establishing NVIDIA as a major player in the 3D graphics market.

The GeForce 256: A Game-Changer:
NVIDIA’s fifth chip, the GeForce 256, was released in late 1999 and marked a significant milestone. It was the world’s first programmable GPU, combining acceleration with programmability. This innovation led to a surge in creativity and popularity in video games.

NVIDIA’s IPO and Partnership with Microsoft:
NVIDIA went public in 1999 and secured a major partnership with Microsoft. Microsoft chose NVIDIA’s GeForce GPUs to power its new Xbox console.

Challenges and Market Expansion:
Despite its success, NVIDIA faced competition from other players in the 3D graphics market. To address this, the company aimed to expand beyond the PC gaming market and explore new opportunities.

Concerns about Market Growth and Competition:
NVIDIA recognized the potential limitations of the PC gaming market and the challenges posed by competitors like Intel and AMD. The company sought to create a new market to avoid being constrained by these factors.

Introduction of CUDA and its Impact on Non-Gaming Applications:
The invention of the programmable GPU by NVIDIA opened up new possibilities beyond gaming. In 2006, the release of CUDA, a general-purpose programming interface, enabled GPUs to be used for various applications such as molecular dynamics, seismic processing, CT reconstruction, and image processing.

The Appeal of GPUs for Research and Personal Supercomputing:
Researchers and graduate students discovered the potential of gaming GPUs for their research. By adding a GeForce card to their computers, they could create personal supercomputers, enabling them to conduct research that was previously infeasible.

Quantum Chemist’s Personal Supercomputer:
A quantum chemist in Taiwan built his own personal supercomputer using an array of GeForce cards, allowing him to complete his research within his lifetime.

Andrew Ng’s Work on Deep Learning and GPUs:
Around 2008-2009, Andrew Ng and his students began exploring the use of GPUs for deep learning and neural networks. The availability of sufficient data and the introduction of CUDA technology facilitated this breakthrough.

Building a GPU Server for Deep Learning Experiments:
Andrew Ng and his student, Ian Goodfellow, constructed a GPU server in a dorm room to conduct their deep learning experiments. This server enabled them to achieve significant speedups in training neural networks, ranging from 10x to 100x faster compared to traditional methods.

Simultaneous Efforts in Toronto and New York:
Hinton’s lab in Toronto and LeCun’s lab in New York University were also conducting similar research on deep learning and GPUs. These groups independently realized the potential for a new type of computing model.

The Uncertainty of the AI Market:
The AI market in 2012-2015 was virtually nonexistent, presenting a significant risk for companies investing in AI chips. There was no guarantee that AI would materialize as a viable market opportunity, given its history of stops and starts over the past 40 years.

NVIDIA’s Dilemma: To Stay in Its Lane or Venture into Unproven Territory:
NVIDIA faced the decision of whether to focus on its core gaming market or stake its future on the unproven but potentially enormous AI market.

Decision to Invest in AI Computing:
NVIDIA recognized the growing demand for computation beyond traditional graphics and gaming applications. CEO Jensen Wong demonstrated courage and vision by allocating significant resources to AI computing, despite the risks and potential impact on the company’s core business.

Challenges of the Pivot:
The shift towards AI computing required NVIDIA to add costs, hire new talent, and learn new skills. The company’s focus was diverted away from its traditional day-to-day competition in computer graphics and gaming, affecting the entire organization.

Positive Reinforcement and Validation:
NVIDIA sought feedback from AI researchers, who provided positive reinforcement and validation of the company’s direction. Small wins and early successes were celebrated and highlighted to maintain momentum and motivation during the transition.

NVIDIA’s Leadership in the AI Revolution:
NVIDIA’s commitment to AI computing positioned the company as a spearhead of the AI revolution. Adoption of NVIDIA’s GPUs by large-scale data centers and cloud service providers, along with diverse applications across industries, confirmed the potential of AI computing.

Huang’s Law and Exponential Advancements:
NVIDIA’s advancements in AI and computing are following a compounded exponential growth pattern, termed “Huang’s Law.” This new pace of growth, a million times every 10 years, enables the rapid solving of problems that were previously thought to take decades.

Impact on Problem Solving and Timeframes:
The rapid advancement of computing changes the timeframe for problem-solving. Problems that were projected to take 30 years to solve can now be solved in five years due to exponential growth. This has significant implications for industries and technologies that rely on computing power.

NVIDIA’s Expansion Beyond Gaming:
NVIDIA’s platform now extends beyond gaming to include data centers, cloud computing, and anything that can be simulated. NVIDIA is developing hardware and software for applications such as digital twins, climate modeling, and drug discovery.

The Time Span of Discretion:
Sequoia Capital’s concept of the “time span of discretion” emphasizes the importance of long-term thinking in business. NVIDIA’s 30-year journey to fruition exemplifies the impact of sustained vision and perseverance.

The CEO’s Role in Innovation:
CEOs must be bold and ambitious in identifying problems that the company can solve that others may not imagine. They should believe in the company’s potential to achieve more than it currently believes possible.

NVIDIA’s Success as an Outlier:
NVIDIA’s success defies conventional wisdom and expectations. The company’s achievements are the result of focused effort, existential urgency, and the contributions of exceptional individuals.