YouSaid · the spoken record

Jensen Huang

lines on the record
160
first
2026-03-23
most recent
2026-03-23
sittings or episodes
1
sources
podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

  1. “I could reason through it just systematically. We started out as an accelerator company. But the problem with accelerators is that the application domains too narrow. It has to benefit of being incredibly optimized for the job. You know, any specialist has that benefit. The problem with intense specialization is that, of course, your market reach is narrower. But that's even fine. The problem is. Market size also dictates your R&D capacity. And your R&D capacity ultimately dictates the influence and impact that you can possibly have in computing. And so when we first started out as an accelerator, very specific accelerator, we always knew that that was going to be our first step. We have to find a way to become accelerated computing. But the problem is when you become a computing company,”

    2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source

  2. “You know what I'm saying? Yeah. And the reason for that is because the people who are on the staff know when to pay attention. They're supposed, you know, something they could have contributed to, they didn't contribute to. I'm going to call them out, you know, and so, hey, come on, let's get in here.”

    2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source

  3. “And no conversation is ever one person. That's why don't do one-on-ones. We present a problem and all of us attack it. Because we're doing extreme code design and literally the company is doing extreme co design all the time.”

    2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source

  4. “Almost all of them Experts in memory, there's experts in CPUs, there's experts in optical. Yeah, GPUs and. Architecture, algorithms, design”

    2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source

  5. “Might direct staff as 60 people I don't have one on ones with them because it's impossible. You can't have 60 people on your staff if you're going to get work done and”

    2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source

  6. “The number of computers. And then the third question is, how is it? How do you do it? And that's kind of the miracle of this company. You know, when you're designing a computer, you have to have operating system of computers. When you're designing a company, you should first think about what is it that you want the company to produce? You know, I see a lot of companies' organization charts, and they all look the same. Hamburger organization charts, soft organization charts, and car company organization charts, they all look the same. And it doesn't make any sense to me. You know, the goal of a company is to be the machinery, the mechanism. The system that produces the output, and that output is the product that we like to create. It is also designed the architecture of the company should reflect the environment by which it exists. It almost directly says what you should do with the organization”

    2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source

  7. “It all together. There's the first question, which is what is extreme co design? You're optimizing across the entire stack of software, from architectures to chips to systems, to system software to the algorithms to the applications. That's one layer. The second thing that you and I just talked about goes beyond CPUs and GPUs and networking chips and scale up switches and scale out switches. And then, of course, you got to include power and cooling and all of that because, you know, all these computers are extremely, extremely power hungry. They do a lot of work and they're very energy efficient, but they in aggregate still consume a lot of power. And so that's one, the first question is, what is it? The second question is, why is it? And we just spoke about the reason you want to distribute the workload so that you can exceed the benefit of just increasing”

    2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source

  8. “We scale up linearly, or we scale up based on the capabilities of Moore's law, which has largely slowed because Dernard's scaling has slowed.”

    2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source

  9. “Depends on how much of the total workload it is. And so if computation represents 50% of the problem and I sped up computation infinitely, like a million times, you know, I only sped up the total workload by a factor of two. Now, all of a sudden, not only do you have to distribute the computation, you have to shard the pipeline somehow, you also have to solve the networking problem. Because you've got all of these computers, they are all connected together. And so distributed computing at the scale that we do. CPU is a problem, the GPU is a problem, the networking is a problem, the switching is a problem. And distributing the workload across all these computers is a problem. It's just a massively complex computer science problem. And so we just got to bring every technology to bear. Otherwise, we're going to”

    2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source

  10. “Thanks for that question. So, first of all, the reason why Extreme co-design is necessary is because the problem no longer fits inside one computer to be accelerated by one GPU. The problem that you're trying to solve is you would like to go faster than the number of computers that you add. So you added 10,000 computers, but you would like it to go a million times faster. All of a sudden Have to take the algorithm. You have to break up the algorithm. You have to refactor it. You have to shard the pipeline. You have to shard the data. You have to shard the model. Now, all of a sudden, when you distribute the problem this way, not just scaling up the problem but you're distributing. Then everything gets in the way. This is the Amdal's law problem, where the amount of speed up you have for something.”

    2026-03-23 · Lex Fridman Podcast · #494 – Jensen Huang: NVIDIA – The $4 Trillion Company & the AI Revolution · IDENTIFIED FROM THE TRANSCRIPT · source