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What Jensen Huang thinks about scaling laws

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

Co-founder and CEO of NVIDIA since 1993.

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

  1. Our readingThe ChatGPT moment for robotics already happened a couple of years ago; what remains is the unglamorous post-training work.

    Their wordsI would say the chat GPT moment of robots happened a couple of years ago already.

    ↗Jensen Huang: The Mindset That Built NVIDIAyoutube.com 15th of 20 in this recording

    OpenAIrobotics

  2. 3 months earlier

  3. Our readingMoore's law now yields about 25% a year, so 10x and 100x leaps can only come from changing the algorithm and how it is computed, every single year.

    Their wordsTPUs like anything else is impacted by Moore's law. And we know that Moore's law is increasing about 25% per year. And so the only way to really get 10x leaps, 100x leaps, is to fundamentally change the algorithm and how it's computed every single year.

    ↗Jensen Huang – Will Nvidia’s moat persist?youtube.com 8th of 24 in this recording

  4. 3 weeks earlier

  5. Our readingTraining is no longer limited by data but by compute, because most of the data models learn from is now synthetic.

    Their wordsThe amount of data that we use to train models is going to continue to scale to the point where we're no longer limited… Training is no longer limited by… Data is now limited by compute. And the reason for that is most of the data is synthetic.

    ↗Jensen Huang: NVIDIA - The $4 Trillion Company & the AI Revolution | Lex Fridman Podcast #494youtube.com 2nd of 23 in this recording

  7. Our readingInference was never going to be the easy, cheap half of AI, because inference is thinking, and thinking is far harder than reading.

    Their wordsthat was always illogical to me because inference is thinking, and I think thinking is hard. Thinking is way harder than reading.

    ↗Jensen Huang: NVIDIA - The $4 Trillion Company & the AI Revolution | Lex Fridman Podcast #494youtube.com 3rd of 23 in this recording

  9. Our readingAfter pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute.

    Their wordsAnd so the next scaling law is the agentic scaling law. It's kind of like multiplying AI. Multiplying AI, we could spin off agents as fast as you want to spin off agents. And so, you know, I… You know, I have four scaling laws.

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

  11. Our readingIntelligence is going to scale by exactly one thing, and that thing is compute.

    Their wordsAnd so this loop, this cycle, is gonna go on and on and on. It kinda comes down to basically intelligence is gonna scale by one thing, and that's compute.

    ↗Jensen Huang: NVIDIA - The $4 Trillion Company & the AI Revolution | Lex Fridman Podcast #494youtube.com 5th of 23 in this recording