
No Priors Ep. 13 | With Jensen Huang, Founder & CEO of NVIDIA
Sarah Guo (host), Jensen Huang (guest), Elad Gil (host), Narrator
In this episode of No Priors, featuring Sarah Guo and Jensen Huang, No Priors Ep. 13 | With Jensen Huang, Founder & CEO of NVIDIA explores jensen Huang Reveals NVIDIA’s Long Game Building the AI Era Jensen Huang traces NVIDIA’s origins from early chip design and accelerated computing bets that most of Silicon Valley initially dismissed, to becoming the core platform for modern AI. He explains how CUDA and architectural consistency, carried on the back of gaming GPUs, enabled NVIDIA to quietly build a developer ecosystem long before deep learning took off. The conversation covers the technical and organizational principles behind NVIDIA’s success, from FP8 and transformer-optimized chips to a bespoke company structure that balances refinement with skunkworks-style exploration. Huang also looks ahead to domain-specific foundation models, robotics, drug discovery, and climate science as the next frontiers for accelerated computing and AI.
Jensen Huang Reveals NVIDIA’s Long Game Building the AI Era
Jensen Huang traces NVIDIA’s origins from early chip design and accelerated computing bets that most of Silicon Valley initially dismissed, to becoming the core platform for modern AI. He explains how CUDA and architectural consistency, carried on the back of gaming GPUs, enabled NVIDIA to quietly build a developer ecosystem long before deep learning took off. The conversation covers the technical and organizational principles behind NVIDIA’s success, from FP8 and transformer-optimized chips to a bespoke company structure that balances refinement with skunkworks-style exploration. Huang also looks ahead to domain-specific foundation models, robotics, drug discovery, and climate science as the next frontiers for accelerated computing and AI.
Key Takeaways
Pick hard, barely-solvable problems as a wedge for new architectures.
NVIDIA focused on applications that general-purpose CPUs couldn’t handle—graphics, molecular dynamics, seismic processing, then AI—using those “barely possible” problems to justify an accelerated computing architecture and sustain R&D.
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Build one stable architecture and stay maniacally compatible over time.
Huang stresses that every NVIDIA chip was made CUDA-compatible even when few used it, sacrificing margins to ensure developers could target a single, stable instruction set—mirroring x86 or ARM and enabling the later AI boom.
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Balance generality and specialization to maintain real acceleration.
If accelerators become too general, they turn into CPUs and lose their edge; if too narrow, the market stays too small. ...
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Go up the stack only as far as customers need—and no further.
NVIDIA builds not just chips but domain-specific libraries (cuDNN, RTX) and, selectively, foundation models, but only to the point where developers and industries can build on top; they avoid becoming a horizontal AI-model provider.
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Design your company’s org structure as a bespoke architecture, not a template.
Huang rejects generic corporate org charts; he has ~40 direct reports and combines a highly refined execution engine for shipping complex products with agile, shape-shifting skunkworks to explore 10-year bets.
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Cultivate simultaneous conviction and humility as a founder.
He argues entrepreneurs must deeply believe in their core thesis (e. ...
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Future AI breakthroughs will come from domain-specific foundation models.
Huang expects specialized models for proteins, chemicals, climate, 3D worlds, and robotics to be built on top of NVIDIA’s platform, unlocking drug discovery, Earth-scale climate simulation, and general robotic control from language and video.
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Notable Quotes
“We decided to start a company on accelerated computing… to solve problems that normal computers can’t.”
— Jensen Huang
“For the very first time in the history of computing, the language of programming a computer is human.”
— Jensen Huang
“If you are doing something that’s barely possible, you call us.”
— Jensen Huang
“We try to do as little as we can, as much as necessary.”
— Jensen Huang
“Ignorance is one of the superpowers of an entrepreneur, and you’ll never get it again.”
— Jensen Huang
Questions Answered in This Episode
How far can accelerated computing push beyond CPUs before hitting its own limits, and what might replace GPUs in the long term?
Jensen Huang traces NVIDIA’s origins from early chip design and accelerated computing bets that most of Silicon Valley initially dismissed, to becoming the core platform for modern AI. ...
Get the full analysis with uListen AI
What tradeoffs did NVIDIA face in keeping every GPU CUDA-compatible despite poor margins, and were there moments they nearly abandoned that strategy?
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How should a startup today decide where to draw the line between being a platform provider versus building full-stack applications or models?
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What does a “robotic foundation model” realistically look like in five to ten years, and which industries will feel its impact first?
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If you were starting NVIDIA in 2025 rather than the 1990s, what would you do differently given today’s AI and hardware landscape?
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Transcript Preview
Thank you for doing this with us, Jensen.
Delighted to do it, Sarah. Delighted.
Why don't we start at the beginning? Uh, you worked at LSI and AMD before starting a company. How did that happen?
They gave me a job. (laughs)
(laughs)
(laughs)
Let's see, uh, let... I was at Oregon State University, and, uh, uh, it was, um, a campus company day, and, uh, I interviewed at a lot of companies. And, and, um, uh, uh, two companies really, really, um, uh, connected with me. I, I love designing chips and designing computers, and, uh, at the time, in our lab, in the computer science lab, there was a, a poster of a 29,000, um, 32-bit, uh, CPU from AMD. And, uh, uh, it... you know, I always thought it'd be kinda cool to build that. Uh, on the other hand, uh, there was a- another company that was a startup at the time, uh, built by, uh, one of the legends of Silicon Valley, Wulf Corrigan. And, uh, they started a company to design chips using software, uh, to design chips not by hand, but by, by, uh, uh, using programmable, uh, logic. And, and you would describe it in language, and it would, it would synthesize it to chips. And of course, uh, I chose to go to AMD. Uh, it turned out, it turned out that I went there to design microprocessors and, and, uh, my lab partner... not my lab, but my office mate, uh, ended up going to LSI. And, and she, uh, uh, insisted that I, I go there, uh, after I was there and after, after she went there. And, and, um, uh, the LSI team, uh, said, "Hey, we were recruiting this kid from, from Oregon State, and we really wanted him to come work at LSI Logic." And, and it turned out to have been her office mate. And so they all reached out to me, and, and I decided to go there because, uh, it was at the beginning of the EDA industry. It was at the beginning of, uh, designing, uh, chips using computers. And, uh, it was probably one of the... one of the best things that's ever, ever happened to me. And, um, uh, it was in the beginning of the, the, uh, ability for every company to build their own chips, and it's the reason why, uh, I met some really great computer architects. Uh, uh, Andy Bechtolsheim was the founder of Sun. I got to work with, uh, uh, a bunch of great architects at Silicon Graphics and, uh, Jon Rubinstein, who, who, um, uh, was at a company called Dana Computer, who became the vice president of engineering for Apple. And, and so I... uh, in, in a very early age, I got the opportunity to work... And then, of course, uh, the two founders of NVIDIA, uh, Chris Malkowski and, uh, Curtis Payne, myself. And, and so I got a chance to work with some really amazing computer architects, and I learned a lot about, uh, about building computers with chips. And so that's, you know, my early days.
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