Read the supporting quotation · NVIDIA · 2026-02-05
“a gigawatt AI factory is about $50,000,000,000 And now we're building tens of gigawatts around the world. It's an enormous infrastructure build out, the largest industrial infrastructure build out in human history.”
Huang estimates a trillion dollars of AI infrastructure to be built over the next four to five years. [2]
Read the supporting quotation · BG2 Pod · 2024-10-13
“What about, call it $150,000,000,000 into it? Right. Okay. So we have a trillion dollars of infrastructure to go build over the next four or five years.”
Huang compares AI factories to electricity generation, saying tokens will be sold per million like kilowatt-hours. [3]
Read the supporting quotation · Stripe · 2024-05-21
“And just like the last industrial revolution, nobody understood why this electricity is so valuable and is now sold, right, marketed kilowatt kilowatt hours, you know, per dollar.”
FROM THE RECORD · 11 matching entries · 2024-10-13 to 2026-06-16
What does the record say about infrastructure?
Selected points from existing editorial summaries and source excerpts. This covers the material on this page.
1
Huang says xAI built a 100,000 GPU supercomputer in nineteen days versus the normal four-year timeline. [1]
Read the supporting quotation · BG2 Pod · 2024-10-13
“100,000 GPUs, that's easily the fastest supercomputer on the planet as one cluster. A supercomputer that you would build would take normally three years to plan.”
Read the supporting quotation · NVIDIA · 2026-02-05
“a gigawatt AI factory is about $50,000,000,000 And now we're building tens of gigawatts around the world. It's an enormous infrastructure build out, the largest industrial infrastructure build out in human history.”
Huang forecasts tens of trillions of dollars in new computer infrastructure over the next decade. [3]
Read the supporting quotation · Associated Press · 2026-06-16
“We're now in the process of reinventing all of that, which is the reason why over the course of the next ten years, we're going to be building tens of trillions of dollars of new computers to replace the old computers that we built over the last sixty years.”
FROM THE RECORD · 5 matching entries · 2024-10-13 to 2026-06-16
What does the record say about capex?
Selected points from existing editorial summaries and source excerpts. This covers the material on this page.
1
Huang says NVIDIA will spend about $500 billion building in America. [1]
Read the supporting quotation · Associated Press · 2026-06-16
“The chip plant in TSMC's chip had chip plant in Arizona, Foxconn, the the number of companies that we've inspired from Taiwan to to partner with us to build factories here.”
Huang estimates a trillion dollars of AI infrastructure to be built over the next four to five years. [2]
Read the supporting quotation · BG2 Pod · 2024-10-13
“What about, call it $150,000,000,000 into it? Right. Okay. So we have a trillion dollars of infrastructure to go build over the next four or five years.”
FROM THE RECORD · 5 matching entries · 2025-05-07 to 2026-06-16
What does the record say about manufacturing?
Selected points from existing editorial summaries and source excerpts. This covers the material on this page.
1
Huang says NVIDIA will spend about $500 billion building in America. [1]
Read the supporting quotation · Associated Press · 2026-06-16
“The chip plant in TSMC's chip had chip plant in Arizona, Foxconn, the the number of companies that we've inspired from Taiwan to to partner with us to build factories here.”
Huang notes the U.S. invested $62 billion in chips but discovered no one domestically could build factories. [2]
Read the supporting quotation · Milken Institute · 2025-05-07
“The US passed an act. We're gonna invest 62,000,000,000. Mhmm. And then they discovered six months later, there's no one in The United States that knows how to build a factory.”
Huang says models may run 10,000 internal inferences before answering, including tree search and reinforcement learning. [3]
Read the supporting quotation · BG2 Pod · 2024-10-13
“The idea that a model before it answers your answer had already done internal inference 10,000 times, it's probably not unreasonable. And it's probably done tree search, it's probably done reinforcement learning on that,”
FROM THE RECORD · 4 matching entries · 2024-10-13 to 2026-02-05
What does the record say about performance?
Selected points from existing editorial summaries and source excerpts. This covers the material on this page.
1
Huang contrasts Moore's law (100x per decade) with current AI acceleration (1 million times per decade). [1]
Read the supporting quotation · Cisco · 2026-02-04
“Remember, Moore's law was two times every 18, 10 times every five years, a 100 times every ten. Okay? But where are we now? A million times every ten years.”
Huang claims NVIDIA reduced computing costs 100,000x in ten years versus Moore's Law's 100x. [3]
Read the supporting quotation · BG2 Pod · 2024-10-13
“we drove the marginal cost of computing down by 100000x over the course of ten years. Moore's law would have been about 100x. And we did it it in several ways.”
Huang forecasts tens of trillions of dollars in new computer infrastructure over the next decade. [2]
Read the supporting quotation · Associated Press · 2026-06-16
“We're now in the process of reinventing all of that, which is the reason why over the course of the next ten years, we're going to be building tens of trillions of dollars of new computers to replace the old computers that we built over the last sixty years.”
Selected points from existing editorial summaries and source excerpts. This covers the material on this page.
1
Huang states a gigawatt AI factory costs approximately fifty billion dollars and tens are being built worldwide. [1]
Read the supporting quotation · NVIDIA · 2026-02-05
“While these factories are incredibly complex, they're you know, a gigawatt AI factory is about $50,000,000,000 And now we're building tens of gigawatts around the world.”
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Quoted in printThe Times of India
“I love the heck out of Bill ⦠but I don't see what he sees. I see something very, very different. And so my remedies will be a little different.”
Bill Gates, Nvidia CEO Jensen Huang does not agree with you; says: I love Bill, but he is wrong as ... · TOI Tech Desk
Quoted in printThe Times of India
“When companies are more productive, they don't lay off people, they hire more people. The reason for that is because companies have ambitions ⦠and I would say the vast majority of the world's companies ⦠have ambitions for growth. When they're more productive, when they're more profitable, [it] allows us to invest more and go after more growth. Nonetheless, overall, this is going to be a net job creator at a scale that we have never seen.”
‘I’m in favor of taxes,’ says Nvidia’s Jensen Huang—but he doesn’t agree with Bill Gates on his plan to slow an AI fallout · Eleanor Pringle
Quoted in printStockPil
“We have also repriced our products in the marketplace, and based on that, our margins are going to come down from 75%. But it will be between 72% and 73% next year.”
Nvidia CEO Jensen Huang defends 'ripping the Band-Aid off' on gross margins as AI chip costs surge · Benjamin
Quoted in printStockPil
“There’s just not enough skilled labor around the world and surely not here in the United States to be able to support my entire demand. But we have enough support for the 70% that I’ve forecasted,”
Nvidia CEO Jensen Huang defends 'ripping the Band-Aid off' on gross margins as AI chip costs surge · Benjamin
Quoted in printCNBC
“ the company said.
-- Lora Kolodny
In an interview with CNBC's Mad Money, Huang gave some more details on the company's agreement with AWS.
He said in addition to the 2 million GPUs that Amazon plans to buy, the cloud company can purchase ”
Nvidia earnings takeaways: Huang forecasts 70% fiscal 2028 revenue growth, far above estimates · Kif Leswing
Quoted in printCNBC
“
— Kif Leswing
Nvidia said it expects gross margin to decline and bottom out in the fourth quarter of fiscal 2027, in the range of 71% to 72%, partially due to memory prices.
”
Nvidia earnings takeaways: Huang forecasts 70% fiscal 2028 revenue growth, far above estimates · Kif Leswing
Transcript excerptAssociated Press
“Since the invention of the IBM System three sixty in 1964, we're now reinventing the computer. You no longer have to code and no longer record information in advance, prerecording information.”
“We're now in the process of reinventing all of that, which is the reason why over the course of the next ten years, we're going to be building tens of trillions of dollars of new computers to replace the old computers that we built over the last sixty years.”
“So all of that combination of social norms, regulations, safer cars, seat belts, and all the technology that comes along with it, it's it's all of it at the same time.”
“And he was delighted that we invested in Intel. He's delighted that we're investing in Coherent. He's incredibly happy that we are manufacturing in United States.”
“He's delighted that we're investing in Coherent. He's incredibly happy that we are manufacturing in United States. We're gonna spend about $500,000,000,000 building in America.”
“The chip plant in TSMC's chip had chip plant in Arizona, Foxconn, the the number of companies that we've inspired from Taiwan to to partner with us to build factories here.”
“We're gonna take almost one year to build a four x. Are you guys the scale up is incredible. That tells you about the incredible demand that we have for accelerated computing,”
“PhysicsNEMO is essentially a physics aware AI model simulation system and AI framework that allows us to create these AI models that are either trained by principled simulators or work alongside principled simulators”
“it's grounded in the laws of physics, but able to predict 10,000 times faster. And now, if everything is already running in real time, then you can predict it 10,000 times greater scale.”
“Right now, as we're speaking, these three entirely what used to be three different industries are all growing incredibly fast so that we could create the infrastructure for intelligence and manufacturing the AIs.”
“While these factories are incredibly complex, they're you know, a gigawatt AI factory is about $50,000,000,000 And now we're building tens of gigawatts around the world.”
“a gigawatt AI factory is about $50,000,000,000 And now we're building tens of gigawatts around the world. It's an enormous infrastructure build out, the largest industrial infrastructure build out in human history.”
“But remember what computing is. There's computing, there's the processing, but there's storage, networking, and security. All that is being reinvented as we speak.”
“Remember, Moore's law was two times every 18, 10 times every five years, a 100 times every ten. Okay? But where are we now? A million times every ten years.”
“In the last ten years, we advanced AI so, so far that engineers said, hey, guess what? Why don't we just train an AI model on all of the world's data?”
“That's the definition of abundance. The definition of abundance is you look at a problem so big and you say, you know what? I'll do it all. I'm gonna cure every field of disease.”
“The IT industry is about a trillion dollars, right, or so, plus or minus a couple. And yet the the economy of the world is about a $100,000,000,000,000.”
“So it is the it is the case that all of you, all of you, everybody in in this room today, you have the opportunity to apply this technology to become a technology company.”
“I am not confident. I am not secure about putting all of NVIDIA's conversations in the cloud, which is the reason why we build it locally. We've built a super AI system locally.”
“We estimated a number of years ago that if you took the most modern agricultural technology, that in the world you might eliminate a half a billion jobs of what's going on in farming, subsides, etcetera.”
“The US passed an act. We're gonna invest 62,000,000,000. Mhmm. And then they discovered six months later, there's no one in The United States that knows how to build a factory.”
“100,000 GPUs, that's easily the fastest supercomputer on the planet as one cluster. A supercomputer that you would build would take normally three years to plan.”
“A supercomputer that you would build would take normally three years to plan. Right. And then they deliver the equipment and it takes one year to get it all working.”
“we drove the marginal cost of computing down by 100000x over the course of ten years. Moore's law would have been about 100x. And we did it it in several ways.”
“What about, call it $150,000,000,000 into it? Right. Okay. So we have a trillion dollars of infrastructure to go build over the next four or five years.”
“there's gonna be a new infrastructure. This new infrastructure are going to be AI factories that operate these digital humans. And they're gonna be running all the time, 20 fourseven.”
“The idea that a model before it answers your answer had already done internal inference 10,000 times, it's probably not unreasonable. And it's probably done tree search, it's probably done reinforcement learning on that,”
“Now, you created the conditions because of some mistake that you made or silliness that you brought upon yourself. We should all learn from that opportunity.”
“CUDA added a ton of cost into our chips, but there were no applications. Right. And there are no applications customers don't value the product and they won't pay you a premium for it.”
“The world has installed about a trillion dollars worth of data centers. Those trillion dollars worth of data centers use as general purse general purpose computing.”
“And just like the last industrial revolution, nobody understood why this electricity is so valuable and is now sold, right, marketed kilowatt kilowatt hours, you know, per dollar.”
“Over the course of seven years, it's now superhuman. Same thing with speech recognition, same things with with natural language understanding. We want to know.”
