Jensen Huang

Founder, President & CEO, NVIDIA

About

Jensen Huang (Jen-Hsun Huang) is the founder, president, and CEO of NVIDIA, the company whose 1993 founding from a Denny's restaurant led to the invention of the GPU in 1999 — a breakthrough that transformed gaming, scientific computing, and ultimately enabled the deep learning revolution. Born in Taiwan in 1963, he moved to the United States as a child, earned his BSEE from Oregon State University and MSEE from Stanford. Under his leadership, NVIDIA became the world's most valuable company, powering the infrastructure behind modern AI. He is widely regarded as the architect of the GPU computing era.

Key Contributions

Co-founded NVIDIA in 1993 and bet on accelerated graphics before GPUs became the engine of modern AI
Led NVIDIA from near-failure into GPU leadership, first through graphics and later high-performance computing
Backed CUDA and general-purpose GPU computing, turning gaming hardware into a platform for deep learning and scientific workloads
Built NVIDIA's data-center stack — GPUs, networking, CUDA libraries, and DGX systems — into the default infrastructure for frontier AI labs
Led NVIDIA through the generative-AI boom to become one of the world's most valuable companies
Also became a symbol of AI's hardware bottleneck: supply constraints, export controls, and dependence on a single vendor now shape the field

Moments Timeline View all moments

Jul 2026

Open Weights and American AI Leadership

As Washington weighs banning Chinese open-weight models over the Kimi K3 accusation, Jensen Huang publishes 'Open Weights and American AI Leadership' — his first-ever post on X — initially signed by 25 companies including Nvidia, Microsoft, Meta, IBM, Hugging Face, Mozilla, and the Linux Foundation. The letter defends distillation as 'a widely used technique for model improvement,' argues open models strengthen rather than weaken security, and urges targeted enforcement over category-wide bans. Within days it gathers over 270 signatories; OpenAI, Google, and Amazon sign after first abstaining. Anthropic — the alleged victim of the distillation — stays out. On July 27, Dario Amodei publishes the company's own position instead: Anthropic 'has never advocated for a ban on open-weights models' and calls safe open models 'a public good,' but asks for three things the letter does not — tighter chip export controls, a crackdown on industrial-scale distillation, and mandatory safety testing for any sufficiently capable model. The episode maps the industry's fault line: who profits from open weights, who is exposed by them, and whether openness is a strategic asset or an attack surface.

Videos & Interviews

Conversation with Jensen Huang, President and CEO of NVIDIA | WEF Annual Meeting 2026

Jensen Huang discusses AI, computing, and NVIDIA's vision at the World Economic Forum in Davos 2026

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Joe Rogan Experience #2422 - Jensen Huang

Wide-ranging conversation on the AI revolution, NVIDIA's origins, fear of failure, and the future of computing

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NVIDIA CES 2026 Keynote with CEO Jensen Huang

NVIDIA's CES 2026 keynote covering next-gen chips, robotics, AI infrastructure, and the future of computing

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Connections

Ilya Sutskever

In conversation

Co-founder, Safe Superintelligence Inc.

In 2012 Sutskever and Krizhevsky trained AlexNet on two consumer NVIDIA gaming cards, and Huang has called the result the big bang of AI — the moment a graphics company discovered what its chips were actually for. Eleven years later they sat down together at GTC, the day after GPT-4 launched, and Huang put the arithmetic plainly: over the decade they had known each other, the models Sutskever trained had grown roughly a million times. The exchange is the clearest record of how completely the supply of compute and the ambition of research have come to define one another.

blogs.nvidia.com · en.wikipedia.org

Jeff Dean

In contrast

Chief Scientist, Google DeepMind

An opposition of strategies rather than of persons. Huang's answer to AI's compute problem was to make one company's chips and software the substrate everyone else rents; Dean's Google built the TPU so that it would not have to rent. Between them sits the field's quiet constitutional question — whether the machinery of intelligence should be a market with one dominant supplier or something each large actor makes for itself — and every frontier lab now sits somewhere on that line.

Chris Lattner

In contrast

Co-founder & CEO, Modular

Modular exists because of what NVIDIA built. CUDA turned a graphics company into the substrate of AI by making its own hardware the only place the software was pleasant to write; Lattner's answer — first MLIR, then Mojo — is to attack that lock-in at the compiler layer, so performance need not be tied to one vendor's ecosystem. Whether AI compute stays a moat or becomes a commodity is being decided in this unglamorous stratum, not at model launches.

Nathan Lambert

Kindred

AI Researcher & Author, Interconnects

Both argue that open weights serve American interests, and the reasons could hardly be more different. Huang published his 2026 letter as the vendor whose business improves when models become a commodity and compute becomes the scarce thing; Lambert argues from the researcher's side, where a model you cannot inspect, retrain, or reproduce is not really an object of study. The pairing is worth holding because it shows openness is not one position but a place where a commercial interest and a scientific one happen to point the same way.