How Jensen Huang transformed Nvidia into a tech giant: From gaming graphics to AI superpower

Nvidia lost its long-term CEO on Thursday after Jensen Huang resigned from his post on Thursday after a string of controversies involving his personal life.

Huang, one of the co-founders, led Nvidia for 33 years. The company has since appointed an interim CEO in long-serving Chief Financial Officer, Colette Kress. The company is looking for a full team CEO and will look for both internal and outside candidate, according to a statement from Nvidia.

Nvidia's transformation from a maker of graphics cards for video games into one of the world's most influential semiconductor companies has been closely linked to the vision and strategic decisions of its co-founder Jensen Huang.

Quick answers to key questions

•5 QUESTIONS

1

How did Nvidia grow from a gaming graphics company into an AI leader?⌵

Nvidia expanded beyond gaming GPUs by investing in software and computing infrastructure. Its GPUs, AI-focused systems and software ecosystem now support AI models, data centres and high-performance computing.

2

What role did CUDA play in Nvidia’s growth?⌵

Introduced in 2006, CUDA let developers use Nvidia GPUs for tasks beyond graphics, including scientific research and simulations. It also helped build a software ecosystem around Nvidia’s chips.

3

Why did Nvidia GPUs become important for artificial intelligence?⌵

GPUs can perform many calculations simultaneously, making them useful for training AI systems. The 2012 AlexNet breakthrough, which used Nvidia GPUs, helped demonstrate their potential for AI.

4

How did Nvidia move from selling chips to building AI infrastructure?⌵

Nvidia expanded its offerings to include AI chips, networking equipment, software and complete data-centre systems. These integrated platforms let customers combine processors, networking and software.

5

Can Nvidia maintain its AI leadership after Jensen Huang’s departure?⌵

The articles identify leadership continuity, product development and customer relationships as key concerns. Nvidia’s established CUDA ecosystem and demand for AI computing are described as advantages, while the board searches for a successor.

Founded in 1993, Nvidia initially focused on developing specialised processors to improve computer graphics and multimedia applications. More than three decades later, its chips power artificial intelligence (AI) models, data centres and high-performance computing systems, making the company a central player in the global AI industry.

Huang's biggest contribution was not simply building faster graphics processing units (GPUs). He recognised early that the technology could have applications far beyond gaming and invested in the software and computing infrastructure needed to unlock that potential.

Nvidia was founded on April 5, 1993, by Jensen Huang, Chris Malachowsky and Curtis Priem. The three engineers wanted to develop advanced 3D graphics technology for the gaming and multimedia markets.

The company's first major product, the NV1, failed to gain significant traction. Its subsequent NV2 development programme was also cancelled, putting the company under financial pressure.

A crucial turning point came when Sega invested in Nvidia following the failure of the NV2 project. Huang later recalled that the funding helped keep the company alive.

The company made a major breakthrough in 1999 with the launch of the GeForce 256, which Nvidia described as the world's first GPU. Unlike traditional processors, GPUs could handle complex graphics calculations, freeing central processing units (CPUs) to perform other tasks.

One of Huang's most consequential decisions came in 2006, when Nvidia introduced CUDA, a parallel computing platform that allowed developers to use GPUs for applications beyond graphics processing.

Traditionally, GPUs were associated with rendering images and running video games. However, their ability to perform thousands of calculations simultaneously made them useful for scientific research, simulations and other computationally intensive tasks.

CUDA opened this capability to developers, researchers and businesses. It also gave Nvidia an advantage beyond the physical performance of its chips by allowing developers to build applications and computing systems around its software ecosystem.

A landmark moment came in 2012, when researchers used Nvidia GPUs to train AlexNet, a neural network that achieved a significant breakthrough in image recognition at the ImageNet competition.

The development demonstrated how GPUs could accelerate the training of AI systems and strengthened the case for using them beyond conventional graphics applications.

Nvidia subsequently made AI a central part of its strategy, investing in processors, software and computing infrastructure designed for machine learning.

When generative AI entered mainstream conversations following the launch of ChatGPT in November 2022, Nvidia was already positioned to benefit from the growing demand for computing power.

Technology companies and AI developers needed increasingly powerful processors to train and run large language models. Nvidia's established GPU technology and software ecosystem gave it a significant advantage as demand for AI computing accelerated.

Under Huang, Nvidia gradually moved beyond supplying individual graphics processors to offering integrated computing platforms.

Its business expanded to include AI chips, networking equipment, software and complete data-centre systems. Its GPU portfolio, including the Hopper and Blackwell architectures, is designed to handle the substantial computing requirements involved in training and running large AI models.

The company also developed high-speed networking technologies that allow thousands of processors to work together within large computing systems.

This strategy helped Nvidia compete on more than the performance of individual chips. It offered customers an integrated platform combining processors, networking and software, making its technology central to the infrastructure used by major technology companies, cloud service providers and AI developers.

The shift is also reflected in Nvidia's revenue profile. According to its fiscal year 2026 annual report, the company recorded revenue of $215.9 billion, up 65% from the previous financial year.

Jensen Huang had to resign on Thursday after his involvement with multiple women came to fore. These include an alleged Chinese spy Alice Huang, who leveraged her relationship with the Nvidia CEO to transfer over 400,000 company's chips to China, The Information reported.

Huang later admitted about his relationship with the alleged Chinese spy.

The ripple effect threw the company's stocks downwards. It lost billions of dollars in market value.

For the potential successor, maintaining technological lead while identifying the next major computing opportunity could be one of the biggest challenges.

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