Learn Our Proven AI Frameworks
Beginners in AI created 6 branded frameworks to help you master AI: STACK for prompting, BUILD for business, ADAPT for learning, THINK for decisions, CRAFT for content, and CRON for automation.
Who Is Jensen Huang?
Jen-Hsun “Jensen” Huang (born February 17, 1963, in Tainan, Taiwan) is the co-founder, President, and CEO of NVIDIA Corporation — the semiconductor company whose Graphics Processing Units (GPUs) became the indispensable hardware of the modern AI revolution. In June 2023, NVIDIA briefly became the world’s most valuable company, crossing a $3.3 trillion market capitalization — a journey from a 1993 startup founded by three engineers at Denny’s diner to the company that makes the chips powering virtually every major AI system on Earth. Understanding Jensen Huang’s story means understanding the GPU revolution in AI.
Huang moved to the United States at age nine, living briefly in Thailand before attending Oneida Baptist Institute in Kentucky (a boarding school his parents chose from a list) and then Aloha High School in Beaverton, Oregon. He earned a BS in electrical engineering from Oregon State University (1984) and an MS in electrical engineering from Stanford University (1992). Between his degrees, he worked as a microprocessor designer at AMD and then as a director at LSI Logic, giving him deep experience in chip architecture before co-founding NVIDIA.
Sources
Get Smarter About AI Every Morning
Free daily newsletter — one story, one tool, one tip. Plain English, no jargon.
Free forever. Unsubscribe anytime.
Founding NVIDIA (1993): The Denny’s Bet
On April 5, 1993, Huang co-founded NVIDIA with Chris Malachowsky and Curtis Priem over a meeting at a Denny’s restaurant in East San Jose, California. The three had worked together at Sun Microsystems and LSI Logic and shared a conviction that 3D graphics would become the next major computing platform. NVIDIA’s name combines the Latin word “invidia” (envy) with “NV” (next vision).
NVIDIA’s early years were genuinely precarious. Its first chip, the NV1 (1995), was a commercial failure. The company nearly went bankrupt multiple times in its first decade. Huang made an extraordinarily risky decision in 1997: rather than wait for sales revenue, he committed NVIDIA’s last $12 million to developing a new chip architecture, the RIVA 128, which shipped in 1997 and became a hit. This pattern — betting the company on a single technical bet — would repeat throughout NVIDIA’s history. It defines how Huang operates. Understanding this risk tolerance helps explain how NVIDIA came to dominate AI hardware.
The GPU: From Gaming to Science
NVIDIA coined the term “Graphics Processing Unit” (GPU) with the GeForce 256 in 1999 — the world’s first single-chip GPU. Unlike a CPU (Central Processing Unit), which has a small number of powerful cores optimized for sequential computation, a GPU has thousands of smaller cores optimized for parallel computation. A GeForce 256 had 22 million transistors and could perform 50 million polygons per second — extraordinary for 1999. It was designed entirely for rendering 3D graphics in video games.
The scientific community noticed something: GPU’s parallel architecture was also exceptionally good at scientific computing tasks — molecular dynamics simulations, weather modeling, protein folding — any computation that could be parallelized across thousands of cores simultaneously. By the mid-2000s, researchers were “hacking” graphics APIs to run scientific code on GPUs. This was difficult, brittle, and hacky. Jensen Huang saw the opportunity and made a decision that would change history. This directly connects to how AI tokens and matrix multiplications underpin modern language models.