In a candid conversation with Nvidia’s co-founder and CEO, Jensen Huang, we get a glimpse into the company’s strategic decisions, its journey into the datacenter business, and the deep roots of its platform strategy.
Huang’s insights provide a comprehensive understanding of Nvidia’s success in the AI sector and its vision for the future.
Origins of Nvidia’s Platform Strategy
The roots of Nvidia’s platform strategy can be traced back to the company’s inception.
The decision to venture into the datacenter business, despite unexpected sources of motivation, highlights Nvidia’s strategic foresight and ability to seize unique opportunities, contributing significantly to its success.
A Mission-Driven Culture
Nvidia’s mission-driven approach fosters collaboration across the organization.
The commonly used phrase within the company, ‘mission is the boss’, aligns the best skills, teams, and resources to achieve their mission, fostering a culture of shared success.
Learning from Others’ Experiences
Huang emphasizes the importance of learning from business books and others’ experiences.
Instead of blindly adopting their strategies, he advises understanding what these lessons mean in the context of one’s own company and environment, and formulating strategies accordingly.
Your organization should be the architecture of the Machinery of building the product. That’s what a company is. Yet everybody’s company looks exactly the same but they all build different things. How does that make any sense? – Jensen Huang
Anticipating Future Opportunities
Huang underscores the importance of anticipating future opportunities and positioning the company to be ready for them.
He believes that CEOs should be able to foresee where opportunities will be, even if they are not exactly sure what and when.
Leadership Pressure in a Non-Traditional Structure
In Nvidia’s non-traditional structure, leaders face high pressure.
Unlike in a command and control system where information is held by those higher up, in Nvidia, information is quickly disseminated to many people at a team level.
This means that leaders earn their positions through their ability to reason through problems and help others succeed, rather than having privileged information.
Resilience Amid Challenges
Nvidia has demonstrated remarkable resilience and adaptability in the face of numerous challenges.
An example of this was in 1997 when the company, on the brink of bankruptcy, decided to test the Reva 128, one of the largest Graphics chips ever created, in simulation rather than using a physical prototype.
Our job as CEO is to look around corners and anticipate where will opportunities be someday and even if I’m not exactly sure what and when, how do I position the company to be near it to be just standing kind of near under the tree and we can do a diving catch when the Apple falls. – Jensen Huang
Vision and Risk-Taking as Key Drivers
Having a clear vision and the courage to take risks are vital in the rapidly evolving field of 3D graphics.
Jensen Huang’s leadership, characterized by strategic decision-making even in the face of adversity, has played a crucial role in Nvidia’s success.
The Revolutionary Impact of Deep Learning
Deep learning has revolutionized traditional computer vision work, offering scalable solutions to a wide range of problems.
Recognizing the potential of machine learning systems across various industries has guided Nvidia’s investment in these technologies.
The Power of Collaboration
Nvidia’s collaborations with universities and researchers have been instrumental in advancing AI and machine learning.
By supporting AI researchers and their work, Nvidia has significantly contributed to the progression of these fields.
Nvidia’s Unique Company Structure
Nvidia’s structure, likened to a computing stack with different layers managed by different individuals, is a reflection of Huang’s belief that a company’s structure should mirror the architecture of the product it builds.
This belief has guided Nvidia’s structure since its early days.
Embracing the Future of Computing
Nvidia’s strategic entry into the datacenter business was based on the foresight that most computing would be done away from the viewing device.
This insight positioned Nvidia advantageously when opportunities in AI and data center arose.
Importance of Experimentation in Machine Learning
Experimentation is critical when deploying new models in machine learning.
AB testing, which involves deploying a model to a subset of users and measuring the impact on core product metrics, has transformed the machine learning development process, promoting a data-driven culture, reducing risk, and speeding up release cycles.
