Jensen Huang is a Taiwanese-American engineer and businessman, co-founder and chief executive officer of NVIDIA, the chipmaker whose graphics processors became the primary hardware powering modern AI training and inference.
Huang was born on February 17, 1963, in Taipei, Taiwan, and moved to the United States as a child. He co-founded NVIDIA in 1993, originally to build graphics chips for video games and computer graphics. The company's GPU architecture, designed for the highly parallel math needed to render 3D scenes, turned out to be well suited to the matrix operations at the core of deep learning, a connection that became commercially decisive only years after NVIDIA introduced the CUDA programming platform in 2006, which let researchers write general-purpose code for GPUs.
From gaming chips to AI infrastructure
NVIDIA's importance to AI became widely visible after the 2012 ImageNet competition, when AlexNet, trained on NVIDIA GPUs, dramatically outperformed prior computer vision approaches, a result often credited with kicking off the modern deep learning era. Huang has described this period as validating a decade of investment in CUDA that had, until then, mostly served niche scientific computing markets rather than a mass-market business.
The generative AI boom
The release of ChatGPT and the subsequent race among labs to train ever-larger large language models drove enormous demand for NVIDIA's data center GPUs, particularly the H100 and later Blackwell chip families. NVIDIA's market capitalization rose sharply during the AI boom, briefly making it the most valuable publicly traded company in the world in 2024, a position it held alongside long-standing rivals such as Apple and Microsoft. Huang became a prominent public figure in this period, known for keynote presentations delivered in his signature black leather jacket, and for framing NVIDIA's role as providing the "picks and shovels" of the AI boom regardless of which model or company ultimately won.
Reception and risk
Analysts have both praised NVIDIA's execution and warned about customer concentration, since a large share of its data center revenue depends on continued heavy capital spending by a handful of AI labs and cloud providers. Export restrictions on advanced chips to China, intended to slow that country's AI progress, also became a recurring point of tension for Huang, who has publicly argued that overly broad restrictions could push Chinese developers toward domestic alternatives instead.