No Priors Ep. 13 | With Jensen Huang, Founder & CEO of NVIDIA

So much of the AI conversation today revolves around models and new applications. But this AI revolution would not be possible without one thing – GPUs, Nvidia GPUs. The Nvidia A100 is the workhorse of today’s AI ecosystem. This week on No Priors, Sarah Guo and Elad Gil sit down with Jensen Huang, the founder and CEO of NVIDIA, at their Santa Clara headquarters. Jensen co-founded the company in 1993 with a goal to create chips that accelerated graphics. Over the past thirty years, NVIDIA has gone far behind gaming and become a $674B behemoth. Jensen talks about the meaning of this broader platform shift for developers, making very long term bets in areas such as climate and biopharma, their next-gen Hopper chip, why and how NVIDIA chooses problems that are unsolvable today, and the source of his iconic leather jackets. 00:00 - Introduction 01:26 - The early days when Jensen Co-founded NVIDIA 04:58 - Why NVIDIA started to expand its aperture to artificial intelligence use cases 10:42 - The moment in 2012 Jensen realized AI was going to be huge 13:52 - How we’re in a broader platform shift in computer science 17:48 - His vision for NVIDIA’s future lines of business 18:09 - How NVIDIA has two motions: Shipping reliable chips and solving new use cases 25:41 - Why no one should assume they’re right for the job of CEO and why not every company needs to be architected as the US military 31:39 - What’s next for NVIDIA’s Hopper 32:57 - Durability of Transformers 35:08 - What Jensen is excited about in the future of AI & his advice for founders

Sarah GuohostJensen HuangguestElad Gilhost

Apr 24, 202352mWatch on YouTube ↗

WHAT IT’S REALLY ABOUT

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.

IDEAS WORTH REMEMBERING

5 ideas

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.

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.

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. NVIDIA’s key skill is walking this line—broadening use cases without giving up outsized speedups.

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.

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.

WORDS WORTH SAVING

5 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

Jensen Huang’s early career and the founding of NVIDIAThe philosophy and evolution of accelerated computing vs. general-purpose CPUsCUDA, GPU architectural consistency, and building a developer platformNVIDIA’s role in the rise of deep learning, transformers, and large language modelsNVIDIA’s business strategy, long-term bets, and organizational designFuture focus areas: robotics, autonomous driving, healthcare, and climate modelingEntrepreneurship lessons, resilience, and the emotional reality of building a company

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