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What Jensen Huang thinks about semiconductors

@jensen-huang · 86 positions · 0 changes of mind

Co-founder and CEO of NVIDIA since 1993.

Everything they publish, on ppll ↗

Jensen Huang did not write this page.

We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.

18 dated positions, 2026, in their own words. Our reading of what Jensen Huang has said — not written or endorsed by them.

Our readingThe argument returned to most often is that the business was never a chip but a whole computing platform, which is also the answer given to every question about custom accelerators. Alongside it runs a claim about time: a company like this has to live five to ten years ahead of its customers.

Their wordswe realized early on uh that it's not about building a great chip, it's about accelerating an algorithm domain.

The business is not building a great chip; it is accelerating an algorithm domain.

  1. Our readingThe business is not building a great chip; it is accelerating an algorithm domain.

    Their wordswe realized early on uh that it's not about building a great chip, it's about accelerating an algorithm domain.

    ↗Jensen Huang: The Mindset That Built NVIDIAyoutube.com 2nd of 20 in this recording

    Interesting

  3. Our readingA computer company has to live five to ten years in the future, because a system takes three years to build, two to ramp, and is then used for a decade.

    Their wordsWe we kind of have to live in the future 5 to 10 years because it takes three or so years just to build a system, takes a couple years to ramp it up, and you're dealing and you would like them to be able to use the computer for 10 years after.

    ↗Jensen Huang: The Mindset That Built NVIDIAyoutube.com 11th of 20 in this recording

    Interesting

  5. Our readingPhysical AI will become one of the largest industries in the world and NVIDIA's next hundred-billion-dollar business, in more than three years but fewer than ten.

    Their wordslikely this will be one of the largest industries in the world and um, uh, it'll take longer than a couple two, three years. It'll take less than 10. And so this will this will be our next $100 billion business.

    ↗Jensen Huang: The Mindset That Built NVIDIAyoutube.com 16th of 20 in this recording

    Interesting
  6. 3 months earlier

  7. Our readingNvidia's business is the transformation of electrons into tokens, and its job is to do as much as necessary and as little as possible in between.

    Their wordsmy mental model of our company the input is electron the output is tokens that is in the middle Nvidia and our job is to to do as much as necessary as little as possible to enable that transformation to be done at incredible capabilities

    ↗Jensen Huang – Will Nvidia’s moat persist?youtube.com 1st of 24 in this recording

    Interesting

  9. Our readingNvidia's suppliers make their investments for Nvidia and nobody else because only Nvidia's downstream demand can absorb what they build.

    Their wordsNow why are they willing to make the investments for me and not someone else and the reason for that is because they know that I have the capacity to buy it buy their supply and sell it through my downstream.

    ↗Jensen Huang – Will Nvidia’s moat persist?youtube.com 3rd of 24 in this recording

    Interesting

  11. Our readingNvidia did not build a tensor processing unit — it built accelerated computing, whose market reach no ASIC can match.

    Their wordsOur market reach is far greater than any any TPU can any ASA can possibly have. And so if you look at our position, uh we're the only company that that accelerates applications of all kinds.

    ↗Jensen Huang – Will Nvidia’s moat persist?youtube.com 6th of 24 in this recording

    Interesting

  13. Our readingA homebuilt accelerator forces you to be your own operator; Nvidia systems are designed for anyone to operate, which is why they are in every cloud.

    Their wordsbecause our computers are designed to be operated by other people, anyone who's an operator could buy our systems. Most of these homebuilt systems you have to be your own operator because it was never designed to be flexible enough for other people to operate.

    ↗Jensen Huang – Will Nvidia’s moat persist?youtube.com 7th of 24 in this recording

    Interesting

  15. Our readingNvidia's engineers can routinely get another 2x out of an AI lab's stack, and doubling the tokens a fleet produces doubles that lab's revenue.

    Their wordsAnd so our expertise um helps our our our um uh our AI labs partners get another 2x out of their stack easily. Often times it's not unusual that we you know by the time that we're done optimizing their stack or optimizing a particular kernel their model sped up by 3x 2x 50%.

    ↗Jensen Huang – Will Nvidia’s moat persist?youtube.com 9th of 24 in this recording

    Interesting

  17. Our readingNo platform in the world has a better performance-per-total-cost-of-ownership ratio than Nvidia's, and nobody will run the public benchmark to show otherwise.

    Their wordsNvidia's computing stack is the best performance per TCO in the world, bar none. Nobody can demonstrate to me that any single platform in the world today has better performance TCO ratio. Not one company.

    ↗Jensen Huang – Will Nvidia’s moat persist?youtube.com 10th of 24 in this recording

    benchmarks

    Interesting

  19. Our readingBuilding an ASIC is not the hard part; building something better than Nvidia is, and that is not easy.

    Their wordsJust because you're going to build an ASIC, you still have to build something better. than Nvidia. And it's not that easy building something better than Nvidia.

    ↗Jensen Huang – Will Nvidia’s moat persist?youtube.com 12th of 24 in this recording

    Interesting

  21. Our readingMoving to a custom ASIC does not escape the margin — the ASIC vendor’s margins are nearly as high as Nvidia’s.

    Their wordsAnd so so I think the the ASIC margins are are incredibly good from what I can tell and and they believe it. They believe it so too. And so they're they're quite proud of their their incredible ASIC margins.

    ↗Jensen Huang – Will Nvidia’s moat persist?youtube.com 13th of 24 in this recording

    Interesting

  23. Our readingNvidia's real miss was not seeing that a frontier lab could never be funded by venture capital, so its compute suppliers had to become its investors.

    Their wordsNor nor did I I would say my mistake is I didn't deeply internalize that they they really had no other options that that that a VC would never put in 510 billion of investment into an AI lab with the with the hopes of it turning out to be anthropic. And so that was my miss.

    ↗Jensen Huang – Will Nvidia’s moat persist?youtube.com 14th of 24 in this recording

    Anthropicstartupsventure capitalinvesting

    Interesting

  25. Our readingNvidia should build only what would not otherwise exist — twenty years of CUDA at a loss is the test, and the world already has plenty of clouds.

    Their wordsthe work that we do with building our our computing platform. If we don't if we don't do it, I genuinely believe it doesn't get done. If we didn't take the risk that we take, if we didn't build MVLink the way we built, if we didn't build the whole stack, if we didn't create the ecosystem the way we did it, if we didn't dedicate ourselves to 20 years of CUDA while losing money most of that time, if we didn't do it, nobody else would have done it.

    ↗Jensen Huang – Will Nvidia’s moat persist?youtube.com 15th of 24 in this recording

    Interesting

  27. Our readingNobody can pick winners: Nvidia's own first graphics architecture was precisely wrong, and it would have topped the list of companies expected to fail.

    Their wordsNvidia would be the top of that list not to make it. You know, this is long before you, but Nvidia's graphics architecture was precisely wrong. It's not a little bit wrong. We created an architecture that was precisely wrong.

    ↗Jensen Huang – Will Nvidia’s moat persist?youtube.com 16th of 24 in this recording

    Interesting

  29. Our readingYou can bet your entire business on Nvidia shipping a better architecture every single year and your token cost falling by an order of magnitude — no ASIC team can promise that.

    Their wordsPick your ASIC team where you can say I can bet the farm of I can bet my entire business that you will be here for me every single year. Your cost, your token cost will decrease by an order of magnitude every single year. I can count on it like I can count on the clock.

    ↗Jensen Huang – Will Nvidia’s moat persist?youtube.com 18th of 24 in this recording

    Interesting

  31. Our readingNvidia does not run rival chip architectures in parallel because it simulates them and they come out provably worse.

    Their wordsYeah, we we could do all of those things. Um it's just not better. And we simulate it all. they're in our simulator provably worse and so we wouldn't do it.

    ↗Jensen Huang – Will Nvidia’s moat persist?youtube.com 23rd of 24 in this recording

    Interesting
  32. 3 weeks earlier

  33. Our readingNVIDIA can be a three-trillion-dollar-revenue company in the near future, because nothing physical stands in the way.

    Their wordsAnd then the rest of it, to me, is: is it possible for NVIDIA to be a, you know, $3 trillion revenue company in the near future? The answer is, of course, yes. And the reason for that is because it's not limited by any physical limits.

    ↗Jensen Huang: NVIDIA - The $4 Trillion Company & the AI Revolution | Lex Fridman Podcast #494youtube.com 15th of 23 in this recording

    Interesting

  35. Our readingThe number of software engineers at NVIDIA will grow, not shrink, because the purpose of a job and the tasks it uses are related but not the same, as radiology already showed.

    Their wordsThe number of software engineers at NVIDIA is gonna grow, not decline. And the reason for that is because the purpose of a software engineer and the task of a software engineer coding are related, not the same.

    ↗Jensen Huang: NVIDIA - The $4 Trillion Company & the AI Revolution | Lex Fridman Podcast #494youtube.com 17th of 23 in this recording

    Interesting