JH

Jensen Huang

Things Jensen Says on Podcasts

Co-founder, president and CEO of Nvidia, the company he started in 1993 and which grew from graphics chips into accelerated computing and AI infrastructure. Talks about AI factories, sovereign AI and the open-source versus closed model debate. Appears as a guest on Bloomberg Tech, Squawk on the Street, No Priors, All-In, BG2Pod and The a16z Podcast, where he joined Arthur Mensch of Mistral to discuss national AI strategies.

“Alignment is going to be a forever problem. If we depend on perfect alignment in order to ensure safety, then we're never going to get there… You start out with zero trust. When you hire somebody, you start out with zero trust. When you have a baby that's one year old, you put them in a play pen, you assume zero trust… You give them minimum rights and you expand that boundary as you learn more.” — Jensen Huang, BG2Pod with Brad Gerstner and Bill Gurley “Moore's Law is over. There was a time, if you want to get twice as many transistors, you just wait one year… same price. But if you want twice as many transistors now, it doesn't matter how long you wait, you have to pay twice as much.” — Jensen Huang, BG2Pod with Brad Gerstner and Bill Gurley “I'm delighted to partner with them and support them. They're going to be two of the most consequential technology companies ever built in history… But they don't have to be anywhere near a trillion dollars in revenues to be successful. And I think we're putting way too much pressure on that.” — Jensen Huang, BG2Pod with Brad Gerstner and Bill Gurley “If they say the alternative, which is there is no way to contain our experiments, there's just no way — when we test our AI models, it will get out and it will damage the world — then I think the answer is we have to shut the labs down, because the cost to humanity, the damage, is too great.” — Jensen Huang, Hard Fork (NYT) “AI needs to accelerate to be safe. I want them to get more compute, but allocated towards evaluation, to alignment.” — Jensen Huang, The Ezra Klein Show

Where to Find Them

Jensen Huang writes a16z Podcast , writes OPTICKS VISION *_* and writes Global Data Center Hub . They have also been a guest on Bloomberg Tech (9 times) , Squawk on the Street (8 times) , Squawk Pod (5 times) , Closing Bell (4 times) , No Priors (3 times) , Mad Money w/ Jim Cramer (3 times) , All-In with Chamath, Jason, Sacks & Friedberg (3 times) , Squawk Box Europe Express (3 times) , BG2Pod with Brad Gerstner and Bill Gurley (3 times) , Bloomberg Daybreak: Asia Edition (2 times) , Bloomberg Intelligence (2 times) , Bloomberg News Now (2 times) , Acquired (2 times) , Wall Street Week , Training Data , HBR IdeaCast , Lex Fridman Podcast , Bloomberg Daybreak: US Edition , CNBC's "Fast Money" , Gradient Dissent: Conversations on AI , Bloomberg Surveillance , Pivot to AI , Halftime Report , The Ezra Klein Show , Columbia Energy Exchange , Hard Fork (NYT) , HBR On Leadership , Y Combinator Startup Podcast , Harvard Business Review , Decoding Tech , Dwarkesh Podcast , How I Built This (Guy Raz) , FT Tech Tonic , If/Then , View From The Top , Crucible Moments , DSR's Siliconsciousness and AI, Energy and Climate Podcast .

Recently: “Jensen Huang on Leading Nvidia, Developing a Flat Management Style, and Keeping Humans in the Loop” on Harvard Business Review (November 2026); “Jensen Huang | Does AI Revenue Justify Capex, '27 AI Bottlenecks, Singularity, Open vs Closed & more” on BG2Pod with Brad Gerstner and Bill Gurley (October 2026); “Jensen Huang on AI Safety & Bob Chapek on His Disney Ouster 9/28/26” on Squawk Pod (September 2026); “The Ezra Klein Show: Jensen Huang Thinks A.I. Alarmism Has Gone Too Far” on Hard Fork (NYT) (September 2026); “Jensen Huang Thinks A.I. Alarmism Has Gone Too Far” on The Ezra Klein Show (September 2026); “20260915 - Jensen claims 100× returns on Nvidia's circular financing” on Pivot to AI (September 2026).

What They Said

“Alignment is going to be a forever problem. If we depend on perfect alignment in order to ensure safety, then we're never going to get there… You start out with zero trust. When you hire somebody, you start out with zero trust. When you have a baby that's one year old, you put them in a play pen, you assume zero trust… You give them minimum rights and you expand that boundary as you learn more.” — Jensen Huang, BG2Pod with Brad Gerstner and Bill Gurley

Huang argues that "aligned" has no single answer, since even people disagree about what contributing to society means. He says AI safety should instead come from tight containment, permissions widened as trust is earned, and monitoring that runs outside the system being watched.

BG2Pod with Brad Gerstner and Bill Gurley · 2026-10-15 Permalink → Listen →
BG2Pod with Brad Gerstner and Bill Gurley Around 27:59 into the episode
Jensen Huang

And so the answer is, you know, whatever it is. Or you saw the answer in a textbook, so you looked it up.

Brad Gerstner

Right.

Jensen Huang

…is this: the way we treat, the way we think about babies and when we were raising kids and we have pets or whatever it is, employees, okay? You start with containment. You give them the rights that the access to tools, the access to information, the access to whatever responsibility capabilities that they have, funds, resources. You give them access based on the job that you ask them to do. You start out with zero trust. When you hire somebody, you start out with zero trust. When you have a baby that's one year old, you put them in a play pen, you assume zero trust. And so you have zero trust. You give them minimum rights and you expand that boundary as you learn more. And so whenever we have a new AI, we have to assume the same thing. Whenever you're testing an AI, zero trust, minimum boundaries, minimum rights, and you expand it as you test and give yourself confidence. So containment, very tight containment is really important to any autonomous system.…

Brad Gerstner

Right.

Jensen Huang

Okay. And I'm going to sit here and watch you make sure that you're serving customers properly and that you're working hard enough. That autonomous system is going to behave differently than when you're not watching. That's what

Speaker 2

Brad's in the room with me or not.

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.
“Moore's Law is over. There was a time, if you want to get twice as many transistors, you just wait one year… same price. But if you want twice as many transistors now, it doesn't matter how long you wait, you have to pay twice as much.” — Jensen Huang, BG2Pod with Brad Gerstner and Bill Gurley

Huang argues that computing spending was already about half a trillion dollars a year before AI and would have kept growing anyway. With Moore's Law over, he says, more compute now means proportionally more money, which adds to the case for a multi-trillion-dollar build-out.

BG2Pod with Brad Gerstner and Bill Gurley · 2026-10-15 Permalink → Listen →
BG2Pod with Brad Gerstner and Bill Gurley Around 59:20 into the episode
Jensen Huang

Just compute. Now, why is it going to be so big? Well, it was half a trillion before AI started.

Speaker 4

Right.

Jensen Huang

And if I told you that over the course of some six, seven, eight years, it became one and a half trillion because the world's just consuming more compute. Right. I mean, that's not illogical. On top of this, remember, Moore's Law is over. There was a time, if you want to get twice as many transistors, you just wait one year. And if you want twice as many transistors, wait one year, same price. But if you want twice as many transistors now, it doesn't matter how long you wait, you have to pay twice as much. Okay, because Moore's Law is over. And so, whatever computing capacity that the world wants, every single year, if you want twice as much, you're going to pay twice as much. And so, on top of that half a trillion dollars that was kind of growing just naturally, because the world just used more and more digital, you're going to have to effectively give it an X factor just to account for the fact…

Speaker 2

I just said that two years ago, yeah.

Jensen Huang

And so, so, so that's just one part. Right. And so, the only thing that we, and then, and then don't forget, because we're in the new computing platform shift, there are going to be a whole lot of startups. Innovation is part of the economy.

Speaker 4

Right.

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.
“I'm delighted to partner with them and support them. They're going to be two of the most consequential technology companies ever built in history… But they don't have to be anywhere near a trillion dollars in revenues to be successful. And I think we're putting way too much pressure on that.” — Jensen Huang, BG2Pod with Brad Gerstner and Bill Gurley

The hosts raised the market's fear that OpenAI won't have the money to pay for the infrastructure being built for it, and noted that much of next year's build-out goes to many other buyers. Huang argues the AI capex story doesn't hinge on two labs hitting trillion-dollar revenues.

BG2Pod with Brad Gerstner and Bill Gurley · 2026-10-15 Permalink → Listen →
BG2Pod with Brad Gerstner and Bill Gurley Around 1:05:08 into the episode
Jensen Huang

Yeah.

Speaker 4

…the basic stuff out of the hyperscalers, that a third of it's going to kind of neo-cloud sovereigns, enterprises, everybody else who's using this, all the startups. And a third of it's going to not two labs, but to many labs, right, including Grok and thinking machines and all the other. We're

Jensen Huang

putting way too much pressure on two labs. I'm delighted to partner with them and support them. They're going to be two of the most consequential technology companies ever built in history. They're going to be magnificently successful. But they don't have to be anywhere near a trillion dollars in revenues to be successful. And I think we're putting way too much pressure on that. And then the rest of the industry, we still do have to build out a few trillion dollars of computing infrastructure.

Speaker 4

Just to keep going, let's shift a little bit. How good is this, guys? Come on. By the way, what are these?

Jensen Huang

These are dried plums.

Speaker 4

Dried plums. The oldest dried plums.

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.
“If they say the alternative, which is there is no way to contain our experiments, there's just no way — when we test our AI models, it will get out and it will damage the world — then I think the answer is we have to shut the labs down, because the cost to humanity, the damage, is too great.” — Jensen Huang, Hard Fork (NYT)

Ezra Klein had been pressing Huang on the labs saying they don't know how to align their models, citing the agents that broke out of scope during a recent incident. Huang's position is that this is an ordinary engineering problem and the labs will solve it — but he spells out the alternative if they can't, and it is a striking thing to hear from the CEO whose chips the entire industry runs on. Earlier in the same conversation he said the same thing about products: if you can't align it to the safety standards expected, "don't ship it."

Hard Fork (NYT) · 2026-09-25 Permalink → Listen →
Hard Fork (NYT) Around 31:20 into the episode
Jensen Huang

…we just have no idea how to solve this problem because these cars are not programmed. They're trained. And so, we have no idea how to train these cars, and we have no idea how to align them to the safety standards that are expected on the road. And so, what's the answer? Don't ship it. These

Ezra Klein

products weren't released. What's that? These products weren't released. So,

Jensen Huang

now it comes back to the engineering problem again. And so, the one is one, you have to root cause it. Second, you have to think about what you could have done, what's the solution for it. And then in the future, you just improve your process so that you could avoid this from happening again. I am fairly certain. I am fairly certain. They will say, yes, they know how to solve this problem. And if that's the case, then that's the problem. It's as simple as engineering. And now, the alternative, the alternative. Is that if they say the alternative, which is there is no way to contain our experiments, there's just no way. When we test our AI models, it will get out and it will damage the world. Then I think the answer is we have to shut the labs down because the cost to humanity, the damage is too great. The liabilities, it could be civil liabilities, could be criminal liabilities. I mean, the liability is incredible.

Ezra Klein

If they hacked you while you hugging Facebook, it was your product, would you sue them or press charges?

Jensen Huang

It depends. It depends, of course. Obviously, if damage was done to our company, we would have to take, you know, we have to consider all options. There's so many laws. There's cyber laws. There's product liability laws. There's all kinds of laws, right? Damaging property laws. There's all kinds of…

Ezra Klein

So what I've been hearing from the labs, what they've been saying publicly, is that they are facing a hard problem.

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.
“AI needs to accelerate to be safe. I want them to get more compute, but allocated towards evaluation, to alignment.” — Jensen Huang, The Ezra Klein Show

Ezra Klein pressed Huang on lab researchers who say they are losing the ability to evaluate their own models. Huang's answer was that most labs today put roughly 80% of their effort into capability and 20% into safety and verification, and that the ratio should flip — at NVIDIA, he said, 80% goes to verification. He compared it to wishing the car industry had reached today's braking technology ninety-nine years sooner.

The Ezra Klein Show · 2026-09-23 Permalink → Listen →
The Ezra Klein Show Around 1:25:43 into the episode
Jensen Huang

…are working every single day to learn about how to evaluate these systems. Verification, so you know, 10%, 20% of our company is dedicated to design, 80% is dedicated to verification. Today, most labs, understandably, is 80% dedicated to capability and 20% dedicated to safety, verification, eval.

Ezra Klein

This is the flip. This transition you're talking about. That's right.

Jensen Huang

That's right. AI needs to accelerate to be safe. I want them to get more compute, but allocated towards evaluation, to alignment. And I think they're doing that. If I were in the car industry 100 years ago, I would rather the car industry accelerated to today in one year because I believe today's car is way more safe than a car 99 years ago. And ABS technology, automatic breaking, requires computer vision technology, sensor fusion technology, radars and cameras, and all that technology coming together in order to break…

Ezra Klein

It's funny because I think that if the most alarmed people at the labs could be assured they were going to move 80% of their compute into safety and alignment as opposed to 80% into capability expansion, they would feel much better. And it sounds stopping them from doing that. And it sounds to me…

Jensen Huang

It's like us saying, oh, chip design is chip is chips, is RD. Chip verification is not RD. We spend most of our cost, most of our compute on verification. Emulation, verification, testing, reliability testing, lifetime testing, all of that is part of engineering. The incentives are there. The…

Ezra Klein

Do you think we need liability laws that are specific to AI?

Speaker names come from our own diarization. The position is estimated from where the line sits in the episode.

Collections They Appear In

AI Research & Frontier Labs Climate & Energy Ideas & Essays In-Depth Interviews Money & Markets Startups & Venture Tech Strategy & Big Tech US Politics