Thirty years ago, Taiwan immigrant Jensen Huang founded Nvidia with the dream of revolutionizing PCs and gaming with 3D graphics. In 1999, after laying off the majority of workers and nearly going bankrupt, the company succeeded when it launched what it claims as the world’s first Graphics Processing Unit (GPU). Then Jensen bet the company on something entirely different: AI. Now, that bet is paying off in a big way as Nvidia’s A100 chips quickly become the coveted training engines for ChatGPT and other generative AI. But as the chip shortage eases, other chip giants like Intel are struggling. And with all it’s chips made by TSMC in Taiwan, Nvidia remains vulnerable to mounting U.S.-China trade tensions. We went to Nvidia’s Silicon Valley, California, headquarters to talk with Huang and get a behind-the scenes-look at the chips powering gaming and the AI boom.

Chapters: 
02:04 — Chapter 1: Popularizing the GPU
07:02 — Chapter 2: From graphics to AI and ChatGPT
11:52 — Chapter 3: Geopolitics and other concerns
14:31 — Chapter 4: Amazon, autonomous cars and beyond

Produced and shot by: Katie Tarasov
Edited by: Evan Lee Miller
Additional Camera: Andrew Evers
Supervising Producer: Jeniece Pettitt
Graphics by: Jason Reginato

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How Nvidia Grew From Gaming To A.I. Giant, Now Powering ChatGPT

This is what hundreds of millions of gamers in the world plays on.

For nearly 30 years, NVIDIA's chips have been coveted by gamers, shaping what's possible in graphics and dominating the entire market since it first popularized the term graphics processing unit with the GeForce 256.

Now its chips are powering something entirely different.

Chats TVT has started a very intense conversation.

Thinks it's the most revolutionary, thinks it's the iPhone.

Venture capital interest in AI startups has skyrocketed.

All of us working in this field have been optimistic that at some point the broader world would understand the importance of this technology and it's it's actually really exciting that that's starting to happen.

As the engine behind large language models like chat GPT, NVIDIA is finally reaping rewards for its investment in AI, even as other chip giants suffer in the shadow of U.S.-China trade tensions and an ease in the chip shortage that's weakened demand.

But the California-based chip designer relies on Taiwan's semiconductor manufacturing company to make nearly all its chips, leaving it vulnerable.

The biggest risk is really kind of U.S.-China relations and the potential impact of TSMC.

That's that if I'm a shareholder in Nvidia, that's really the only thing that keeps me up at night.

This isn't the first time Nvidia has found itself teetering on the leading edge of an uncertain emerging market.

It's neared bankruptcy a handful of times in its history when founder and CEO Jensen Huang bet the company on impossible seeming ventures.

Every company makes mistakes and I make a lot of them.

And some of them puts the company in peril, especially in the beginning, because we're small and we're up against very, very large companies and We're trying to invent this brand new technology.

We sat down with Huang at NVIDIA's Silicon Valley headquarters to find out how he pulled off this latest reinvention and got a behind-the-scenes look at all the ways it powers far more than just gaming.

Now 1 of the world's top 10 most valuable companies, NVIDIA is 1 of the rare Silicon Valley giants that 30 years in still has its founder at the helm.

I delivered the first 1 of these inside an AI supercomputer to open AI when it was first created.

60-year-old Jensen Huang, a fortune businessperson of the year and 1 of Time's most influential people in 2021, immigrated to the U.S.

From Taiwan as a kid and studied engineering at Oregon State and Stanford.

In the early 90s, Huang met fellow engineers Chris Malachowski and Curtis Priam at Denny's, where they talked about dreams of enabling PCs with 3D graphics, the kind made popular by movies like Jurassic Park at the time.

If you go back 30 years, at the time the PC revolution was just starting, and there was quite a bit of debate about what is the future of computing and how should software be run.

And there was a large camp, and rightfully so, that believed that CPU or general purpose software was the best way to go.

And it was the best way to go for a long time.

We felt, however, that there was a class of applications that wouldn't be possible without acceleration.

The friends launched NVIDIA out of a condo in Fremont, California, in 1993.

The name was inspired by Envy for Next Version and NVIDIA, the Latin word for Envy.

They hoped to speed up computing so much everyone would be green with envy.

At more than 80 percent of revenue, its primary business remains GPUs.

Typically sold as cards that plug into a PC's motherboard, they accelerate, add computing power to central processing units, CPUs, from companies like AMD and Intel.

You know, they were 1 among tens of GPU makers at that time.

They are the only ones and them and AMD actually who really survived.

Because they, NVIDIA worked very well with the software community.

This is a business of figuring out things end to end.

But at the start, its future was far from guaranteed.

In the beginning, there weren't that many applications for it, frankly.

And we smartly chose 1 particular combination that was a home run.

It was computer graphics, and we applied it to video games.

Now, NVIDIA is known for revolutionizing gaming and Hollywood with rapid rendering of visual effects.

NVIDIA designed its first high-performance graphics chip in 1997.

Designed, not manufactured, because Hwang was committed to making NVIDIA a fabulous chip company, keeping capital expenditure way down by outsourcing the extraordinary expense of making the chips to TSMC.

NVIDIA today wouldn't be here if, and nor, nor the other thousand fabulous semiconductor companies wouldn't be here if not for the pioneering work that TSMC did.

In 1999, after laying off the majority of workers and nearly going bankrupt to do it, NVIDIA released what it claims was the world's first official GPU, the GeForce 256.

It was the first programmable graphics card that allowed custom shading and lighting effects.

By 2000, NVIDIA was the exclusive graphics provider for Microsoft's first Xbox.

Microsoft and the Xbox happened at exactly the time that we invented this thing called a programmable shader, and it defines how computer graphics is done today.

NVIDIA went public in 1999, and its stock stayed largely flat until demand went through the roof during the pandemic.

In 2006, it released a software toolkit called CUDA that would eventually propel it to the center of the AI boom.

It's essentially a computing platform and programming model that changes how NVIDIA GPUs work from serial to parallel compute.

Parallel computing is let me take a task and attack it all at the same time using much smaller machines.

So it's the difference between having an army where you have 1 giant soldier who is able to do things very well, but 1 at a time, versus an army of thousands of soldiers who are able to take that problem and do it in parallel.

So it's a very different computing approach.

NVIDIA's big steps haven't always been in the right direction.

In the early 2010s, it made unsuccessful moves into smartphones with its Tegra line of processors.

You know, they quickly realized that the smartphone market wasn't for them.

In 2020, NVIDIA closed a long-awaited $7 billion deal to acquire data center chip company Mellanox.

But just Last year, NVIDIA had to abandon a $40 billion bid to acquire ARM, citing significant regulatory challenges.

ARM is a major CPU company known for licensing its signature ARM architecture to Apple for iPhones and iPads, Amazon for Kindles, and many major carmakers.

Despite some setbacks, today NVIDIA has 26,000 employees, a newly built polygon themed headquarters in Santa Clara, California, and billions of chips used for far more than just graphics.

Think data centers, cloud computing and most prominently AI.

We're in every cloud made by every computer company.

And then all of a sudden, 1 day, a new application that wasn't possible before discovers you.

More than a decade ago, NVIDIA's CUDA and GPUs were the engine behind AlexNet, what many consider AI's Big Bang Moment.

It was a new, incredibly accurate neural network that obliterated the competition during a prominent image recognition contest in 2012.

Turns out the same parallel processing needed to create lifelike graphics is also ideal for deep learning, where a computer learns by itself rather than relying on a programmer's code.

He had the good wisdom to go put the whole company behind it.

We saw early on, about a decade or so ago, that This way of doing software could change everything.

We changed the company from the bottom all the way to the top and sideways.

Every chip that we made was focused on artificial intelligence.

Brian Catanzaro was the first and only employee on Nvidia's deep learning team 6 years ago.

For 10 years, Wall Street asked Nvidia, why are you making this investment?

And they valued it at 0 dollars in our market cap.

And it wasn't until around 2016 that after, you know, 10 years after CUDA came out, that all of a sudden people understood this is a dramatically different way of writing computer programs and it has transformational speed-ups that then yield breakthrough results in artificial intelligence.

So what are some real-world applications for NVIDIA's AI?

Think far faster drug discovery and DNA sequencing that takes hours instead of weeks.

We were able to achieve the Guinness World Record in a genomic sequencing technique to actually diagnose these patients and administer 1 of the patients in the trial to have a heart transplant, a 13 year old boy who's thriving today as a result.

And then also a 3 month old baby that was having epileptic seizures and to be able to prescribe an anti-seizure medication.

And then there's art powered by NVIDIA AI, like Rafiq Anadol's creations that cover entire buildings.

And when crypto started to boom, NVIDIA's GPUs became the coveted tool for mining the digital currency.

Which is not really a recommended usage, but that has created problems because crypto mining has been a boom or bust cycle.

So gaming cards go out of stock, prices get paid up.

And then when the crypto mining boom collapses, then there is a big crash on the gaming side.

Although Nvidia did create a simplified GPU made just for mining, it didn't stop crypto miners from buying up gaming GPUs, sending prices through the roof.

And although that shortage is over, NVIDIA caused major sticker shock among some gamers last year by pricing its new 40-series GPUs far higher than the previous generation.

Now there's too much supply and the most recently reported quarterly gaming revenue was down 46 percent from the year before.

But NVIDIA still beat expectations in its most recent earnings report thanks to the AI boom as tech giants like Microsoft and Google fill their data centers with thousands of NVIDIA A100s, the engines used to train large language models like ChatGPT.

When we ship them, we don't ship them in packs of 1.

With a suggested price of nearly $200,000, NVIDIA's DGX-A100 server board has 8 Ampere GPUs that work together to enable things like the insanely fast and uncannily human-like responses of chat GPT.

I have been trained on a massive data set of text, which allows me to understand and generate text on a wide range of topics.

Companies scrambling to compete in generative AI are publicly boasting about how many NVIDIA A100s they have.

Microsoft, for example, trained ChatGPT with 10,000.

It's very easy to use their products and add more computing capacity.

And once you add that computing capacity, computing capacity is basically the currency of the valley right now.

And the next generation up from Ampere, Hopper, has already started to ship.

Some uses for generative AI are real-time translation and instant text-to-image renderings.

But this is also the tech behind eerily convincing, and some say dangerous, deepfake videos, text, and audio.

Are there any ways that NVIDIA is sort of protecting against some of these bigger fears that people have or building in safeguards?

Yes, I think the safeguards that we're building as an industry about how AI is going to be used are extraordinarily important.

We're trying to find ways of authenticating content so that we can know if a video was actually created in the real world or virtually similarly for text and audio.

But being at the center of the generative AI boom doesn't make Nvidia immune to wider market concerns.

Introduced sweeping new rules that banned exports of leading-edge AI chips to China, including NVIDIA's A100.

About a quarter of your revenue comes from mainland China.

How do you calm investor fears over the new export controls?

Well, NVIDIA's technology is export controlled.

It's a reflection of the importance of the technology that we make.

The first thing that we have to do is comply with the regulations.

And it was a turbulent month or so as the company went upside down to re-engineer all of our products so that it's compliant with the regulation and yet still be able to serve the commercial customers that we have in China.

We're able to serve our customers in China with the regulated parts and delightfully support them.

But perhaps an even bigger geopolitical risk for NVIDIA is its dependence on TSMC in Taiwan.

1, will China take over the island of Taiwan at some point?

And 2, is there a viable competitor to TSMC?

And As of right now, Intel is trying aggressively to get there.

This is a risk for AMD, for Qualcomm, even for Intel.

Passed the Chips Act last summer, which sets aside $52 billion to incentivize chip companies to manufacture on U.S.

Now, TSMC is spending $40 billion to build 2 chip fabrication plants, FABs, in Arizona.

The fact of the matter is TSMC is a really important company and the world doesn't have more than 1 of them.

It is imperative upon ourselves and them for them to also invest in diversity and redundancy.

Will you be moving any of your manufacturing to Arizona?

As it largely comes to a close and supply catches up with demand, some types of chips are experiencing a price slump.

But for NVIDIA, the chatbot boom means demand for its AI chips continues to grow, at least for now.

See, the biggest question for them is how do they stay ahead?

Because their customers can be their competitors also.

You know, Microsoft can try and design these things internally.

Amazon and Google are already designing these things internally.

Tesla and Apple are designing their own custom chips too.

But Jensen says competition is a net good.

The amount of power that the world needs in the data center will grow.

And you can see in the recent trends, it's growing very quickly.

While AI and chat GPT have been generating lots of buzz for NVIDIA, it's far from Huang's only focus.

And we take the model and we put it into this computer and that's a self-driving car.

And we take that computer and we put it into here and that's a little robot computer.

Amazon and others use NVIDIA to power robots in their warehouses and to create digital twins of the massive spaces and run simulation to optimize the flow of millions of packages each day.

Driving units like these in NVIDIA's robotics lab are powered by the Tegra chips that were once a flop in mobile phones.

Now they're used to power the world's biggest e-commerce operations.

Nvidia's Tegra chips were also used in Tesla Model 3s from 2016 to 2019.

Now Tesla uses its own chips, but Nvidia is making autonomous driving tech for other car makers like Mercedes-Benz.

And basically, Nvidia drives a scalable platform, whether you want to use it for simple ADAS, assisted driving for your emergency braking warning, pre-collision warning, or just keeping, you know, holding the lane for cruise control, all the way up to a robo-taxi where it is doing everything, driving anywhere in any condition, any type of weather.

NVIDIA is also trying to compete in a totally different arena, releasing its own data center's CPU, Grace.

What do you say to gamers who wish you had kept focused entirely on the core business of gaming?

Well, if not for all of our work in physics simulation, if not for all of our research in artificial intelligence, what we did recently with GeForce RTX would not have been possible.

Released in 2018, RTX is NVIDIA's next big move in graphics, with a new technology called ray tracing.

For us to take computer graphics and video games to the next level, we had to reinvent and disrupt ourselves.

Basically simulating the pathways of light and simulate everything with generative AI.

And so we compute 1 pixel and we imagine with AI the other 7.

Imagine a jigsaw puzzle and we gave you 1 out of 8 pieces.

Ray tracing is used in nearly 300 games now, like Cyberpunk 2077, Fortnite, and Minecraft.

And NVIDIA GeForce GPUs in the cloud allow full-quality streaming of 1,500-plus games to nearly any PC.

It's also part of what enables simulations, modeling of how objects would behave in real-world situations.

Think climate forecasting or autonomous drive tech that's informed by millions of miles of virtual roads.

It's all part of what Nvidia calls the omniverse, what Huang points to as the company's next big bet.

We have 700 plus customers who are trying it now from car industry to logistics warehouse to, you know, to wind turbine plants.

And So I'm really excited about the progress there.

It represents probably the single greatest container of all of NVIDIA's technology, computer graphics, artificial intelligence, robotics, and physics simulation all into 1.


Speaker 0: This is what hundreds of millions of gamers in the world plays on. It's a GeForce. This is the chip that's inside.

Speaker 1: For nearly 30 years, NVIDIA's chips have been coveted by gamers, shaping what's possible in graphics and dominating the entire market since it first popularized the term graphics processing unit with the GeForce 256. Now its chips are powering something entirely different.

Speaker 2: Chats TVT has started a very intense conversation.

Speaker 3: Thinks it's the most revolutionary, thinks it's the iPhone.

Speaker 1: Venture capital interest in AI startups has skyrocketed.

Speaker 4: All of us working in this field have been optimistic that at some point the broader world would understand the importance of this technology and it's it's actually really exciting that that's starting to happen.

Speaker 1: As the engine behind large language models like chat GPT, NVIDIA is finally reaping rewards for its investment in AI, even as other chip giants suffer in the shadow of U.S.-China trade tensions and an ease in the chip shortage that's weakened demand. But the California-based chip designer relies on Taiwan's semiconductor manufacturing company to make nearly all its chips, leaving it vulnerable.

Speaker 5: The biggest risk is really kind of U.S.-China relations and the potential impact of TSMC. That's that if I'm a shareholder in Nvidia, that's really the only thing that keeps me up at night.

Speaker 1: This isn't the first time Nvidia has found itself teetering on the leading edge of an uncertain emerging market. It's neared bankruptcy a handful of times in its history when founder and CEO Jensen Huang bet the company on impossible seeming ventures.

Speaker 0: Every company makes mistakes and I make a lot of them. And some of them puts the company in peril, especially in the beginning, because we're small and we're up against very, very large companies and We're trying to invent this brand new technology.

Speaker 1: We sat down with Huang at NVIDIA's Silicon Valley headquarters to find out how he pulled off this latest reinvention and got a behind-the-scenes look at all the ways it powers far more than just gaming. Now 1 of the world's top 10 most valuable companies, NVIDIA is 1 of the rare Silicon Valley giants that 30 years in still has its founder at the helm.

Speaker 0: I delivered the first 1 of these inside an AI supercomputer to open AI when it was first created.

Speaker 1: 60-year-old Jensen Huang, a fortune businessperson of the year and 1 of Time's most influential people in 2021, immigrated to the U.S. From Taiwan as a kid and studied engineering at Oregon State and Stanford. In the early 90s, Huang met fellow engineers Chris Malachowski and Curtis Priam at Denny's, where they talked about dreams of enabling PCs with 3D graphics, the kind made popular by movies like Jurassic Park at the time.

Speaker 0: If you go back 30 years, at the time the PC revolution was just starting, and there was quite a bit of debate about what is the future of computing and how should software be run. And there was a large camp, and rightfully so, that believed that CPU or general purpose software was the best way to go. And it was the best way to go for a long time. We felt, however, that there was a class of applications that wouldn't be possible without acceleration.

Speaker 1: The friends launched NVIDIA out of a condo in Fremont, California, in 1993. The name was inspired by Envy for Next Version and NVIDIA, the Latin word for Envy. They hoped to speed up computing so much everyone would be green with envy. At more than 80 percent of revenue, its primary business remains GPUs. Typically sold as cards that plug into a PC's motherboard, they accelerate, add computing power to central processing units, CPUs, from companies like AMD and Intel.

Speaker 6: You know, they were 1 among tens of GPU makers at that time. They are the only ones and them and AMD actually who really survived. Because they, NVIDIA worked very well with the software community. This is not a chip business. This is a business of figuring out things end to end.

Speaker 1: But at the start, its future was far from guaranteed.

Speaker 0: In the beginning, there weren't that many applications for it, frankly. And we smartly chose 1 particular combination that was a home run. It was computer graphics, and we applied it to video games.

Speaker 1: Now, NVIDIA is known for revolutionizing gaming and Hollywood with rapid rendering of visual effects. NVIDIA designed its first high-performance graphics chip in 1997. Designed, not manufactured, because Hwang was committed to making NVIDIA a fabulous chip company, keeping capital expenditure way down by outsourcing the extraordinary expense of making the chips to TSMC.

Speaker 0: On behalf of all of us, you're my hero. Thank you. Thank you. NVIDIA today wouldn't be here if, and nor, nor the other thousand fabulous semiconductor companies wouldn't be here if not for the pioneering work that TSMC did.

Speaker 1: In 1999, after laying off the majority of workers and nearly going bankrupt to do it, NVIDIA released what it claims was the world's first official GPU, the GeForce 256. It was the first programmable graphics card that allowed custom shading and lighting effects. By 2000, NVIDIA was the exclusive graphics provider for Microsoft's first Xbox.

Speaker 0: Microsoft and the Xbox happened at exactly the time that we invented this thing called a programmable shader, and it defines how computer graphics is done today.

Speaker 1: NVIDIA went public in 1999, and its stock stayed largely flat until demand went through the roof during the pandemic. In 2006, it released a software toolkit called CUDA that would eventually propel it to the center of the AI boom. It's essentially a computing platform and programming model that changes how NVIDIA GPUs work from serial to parallel compute.

Speaker 6: Parallel computing is let me take a task and attack it all at the same time using much smaller machines. Right? So it's the difference between having an army where you have 1 giant soldier who is able to do things very well, but 1 at a time, versus an army of thousands of soldiers who are able to take that problem and do it in parallel. So it's a very different computing approach.

Speaker 1: NVIDIA's big steps haven't always been in the right direction. In the early 2010s, it made unsuccessful moves into smartphones with its Tegra line of processors.

Speaker 6: You know, they quickly realized that the smartphone market wasn't for them. So they exited right from that.

Speaker 1: In 2020, NVIDIA closed a long-awaited $7 billion deal to acquire data center chip company Mellanox. But just Last year, NVIDIA had to abandon a $40 billion bid to acquire ARM, citing significant regulatory challenges. ARM is a major CPU company known for licensing its signature ARM architecture to Apple for iPhones and iPads, Amazon for Kindles, and many major carmakers. Despite some setbacks, today NVIDIA has 26,000 employees, a newly built polygon themed headquarters in Santa Clara, California, and billions of chips used for far more than just graphics. Think data centers, cloud computing and most prominently AI.

Speaker 0: We're in every cloud made by every computer company. And then all of a sudden, 1 day, a new application that wasn't possible before discovers you.

Speaker 1: More than a decade ago, NVIDIA's CUDA and GPUs were the engine behind AlexNet, what many consider AI's Big Bang Moment. It was a new, incredibly accurate neural network that obliterated the competition during a prominent image recognition contest in 2012. Turns out the same parallel processing needed to create lifelike graphics is also ideal for deep learning, where a computer learns by itself rather than relying on a programmer's code.

Speaker 0: He had the good wisdom to go put the whole company behind it. We saw early on, about a decade or so ago, that This way of doing software could change everything. We changed the company from the bottom all the way to the top and sideways. Every chip that we made was focused on artificial intelligence.

Speaker 1: Brian Catanzaro was the first and only employee on Nvidia's deep learning team 6 years ago. Now it's 50 people and growing.

Speaker 4: For 10 years, Wall Street asked Nvidia, why are you making this investment? No one's using it. And they valued it at 0 dollars in our market cap. And it wasn't until around 2016 that after, you know, 10 years after CUDA came out, that all of a sudden people understood this is a dramatically different way of writing computer programs and it has transformational speed-ups that then yield breakthrough results in artificial intelligence.

Speaker 1: So what are some real-world applications for NVIDIA's AI? Healthcare is 1 big area. Think far faster drug discovery and DNA sequencing that takes hours instead of weeks.

Speaker 2: We were able to achieve the Guinness World Record in a genomic sequencing technique to actually diagnose these patients and administer 1 of the patients in the trial to have a heart transplant, a 13 year old boy who's thriving today as a result. And then also a 3 month old baby that was having epileptic seizures and to be able to prescribe an anti-seizure medication.

Speaker 1: And then there's art powered by NVIDIA AI, like Rafiq Anadol's creations that cover entire buildings. And when crypto started to boom, NVIDIA's GPUs became the coveted tool for mining the digital currency.

Speaker 6: Which is not really a recommended usage, but that has created problems because crypto mining has been a boom or bust cycle. So gaming cards go out of stock, prices get paid up. And then when the crypto mining boom collapses, then there is a big crash on the gaming side.

Speaker 1: Although Nvidia did create a simplified GPU made just for mining, it didn't stop crypto miners from buying up gaming GPUs, sending prices through the roof. And although that shortage is over, NVIDIA caused major sticker shock among some gamers last year by pricing its new 40-series GPUs far higher than the previous generation. Now there's too much supply and the most recently reported quarterly gaming revenue was down 46 percent from the year before. But NVIDIA still beat expectations in its most recent earnings report thanks to the AI boom as tech giants like Microsoft and Google fill their data centers with thousands of NVIDIA A100s, the engines used to train large language models like ChatGPT.

Speaker 0: When we ship them, we don't ship them in packs of 1. We ship them in packs of 8.

Speaker 1: With a suggested price of nearly $200,000, NVIDIA's DGX-A100 server board has 8 Ampere GPUs that work together to enable things like the insanely fast and uncannily human-like responses of chat GPT.

Speaker 7: I have been trained on a massive data set of text, which allows me to understand and generate text on a wide range of topics.

Speaker 1: Companies scrambling to compete in generative AI are publicly boasting about how many NVIDIA A100s they have. Microsoft, for example, trained ChatGPT with 10,000.

Speaker 6: It's very easy to use their products and add more computing capacity. And once you add that computing capacity, computing capacity is basically the currency of the valley right now.

Speaker 1: And the next generation up from Ampere, Hopper, has already started to ship. Some uses for generative AI are real-time translation and instant text-to-image renderings. But this is also the tech behind eerily convincing, and some say dangerous, deepfake videos, text, and audio. Are there any ways that NVIDIA is sort of protecting against some of these bigger fears that people have or building in safeguards?

Speaker 4: Yes, I think the safeguards that we're building as an industry about how AI is going to be used are extraordinarily important. We're trying to find ways of authenticating content so that we can know if a video was actually created in the real world or virtually similarly for text and audio.

Speaker 1: But being at the center of the generative AI boom doesn't make Nvidia immune to wider market concerns. In October, The U.S. Introduced sweeping new rules that banned exports of leading-edge AI chips to China, including NVIDIA's A100. About a quarter of your revenue comes from mainland China. How do you calm investor fears over the new export controls?

Speaker 0: Well, NVIDIA's technology is export controlled. It's a reflection of the importance of the technology that we make. The first thing that we have to do is comply with the regulations. And it was a turbulent month or so as the company went upside down to re-engineer all of our products so that it's compliant with the regulation and yet still be able to serve the commercial customers that we have in China. We're able to serve our customers in China with the regulated parts and delightfully support them.

Speaker 1: But perhaps an even bigger geopolitical risk for NVIDIA is its dependence on TSMC in Taiwan.

Speaker 5: There's 2 issues. 1, will China take over the island of Taiwan at some point? And 2, is there a viable competitor to TSMC? And As of right now, Intel is trying aggressively to get there. And their goal is by 2025.

And we will see. This is not just an NVIDIA risk. This is a risk for AMD, for Qualcomm, even for Intel.

Speaker 1: This is a big reason why the U.S. Passed the Chips Act last summer, which sets aside $52 billion to incentivize chip companies to manufacture on U.S. Soil. Now, TSMC is spending $40 billion to build 2 chip fabrication plants, FABs, in Arizona.

Speaker 0: The fact of the matter is TSMC is a really important company and the world doesn't have more than 1 of them. It is imperative upon ourselves and them for them to also invest in diversity and redundancy.

Speaker 1: Will you be moving any of your manufacturing to Arizona?

Speaker 0: Oh, absolutely. We'll use Arizona. Yeah.

Speaker 1: And then there's the chip shortage. As it largely comes to a close and supply catches up with demand, some types of chips are experiencing a price slump. But for NVIDIA, the chatbot boom means demand for its AI chips continues to grow, at least for now.

Speaker 6: See, the biggest question for them is how do they stay ahead? Because their customers can be their competitors also. You know, Microsoft can try and design these things internally. Amazon and Google are already designing these things internally.

Speaker 1: Tesla and Apple are designing their own custom chips too. But Jensen says competition is a net good.

Speaker 0: The amount of power that the world needs in the data center will grow. And you can see in the recent trends, it's growing very quickly. And that's a real issue for the world.

Speaker 1: While AI and chat GPT have been generating lots of buzz for NVIDIA, it's far from Huang's only focus.

Speaker 0: And we take the model and we put it into this computer and that's a self-driving car. And we take that computer and we put it into here and that's a little robot computer.

Speaker 1: Like the kind that's used at Amazon.

Speaker 0: That's right.

Speaker 1: Amazon and others use NVIDIA to power robots in their warehouses and to create digital twins of the massive spaces and run simulation to optimize the flow of millions of packages each day. Driving units like these in NVIDIA's robotics lab are powered by the Tegra chips that were once a flop in mobile phones. Now they're used to power the world's biggest e-commerce operations. Nvidia's Tegra chips were also used in Tesla Model 3s from 2016 to 2019. Now Tesla uses its own chips, but Nvidia is making autonomous driving tech for other car makers like Mercedes-Benz.

Speaker 3: So we call it Nvidia Drive. And basically, Nvidia drives a scalable platform, whether you want to use it for simple ADAS, assisted driving for your emergency braking warning, pre-collision warning, or just keeping, you know, holding the lane for cruise control, all the way up to a robo-taxi where it is doing everything, driving anywhere in any condition, any type of weather.

Speaker 1: NVIDIA is also trying to compete in a totally different arena, releasing its own data center's CPU, Grace. What do you say to gamers who wish you had kept focused entirely on the core business of gaming?

Speaker 0: Well, if not for all of our work in physics simulation, if not for all of our research in artificial intelligence, what we did recently with GeForce RTX would not have been possible.

Speaker 1: Released in 2018, RTX is NVIDIA's next big move in graphics, with a new technology called ray tracing.

Speaker 0: For us to take computer graphics and video games to the next level, we had to reinvent and disrupt ourselves. Basically simulating the pathways of light and simulate everything with generative AI. And so we compute 1 pixel and we imagine with AI the other 7. It's really quite amazing. Imagine a jigsaw puzzle and we gave you 1 out of 8 pieces.

And somehow the AI filled in the rest.

Speaker 1: Ray tracing is used in nearly 300 games now, like Cyberpunk 2077, Fortnite, and Minecraft. And NVIDIA GeForce GPUs in the cloud allow full-quality streaming of 1,500-plus games to nearly any PC. It's also part of what enables simulations, modeling of how objects would behave in real-world situations. Think climate forecasting or autonomous drive tech that's informed by millions of miles of virtual roads. It's all part of what Nvidia calls the omniverse, what Huang points to as the company's next big bet.

Speaker 0: We have 700 plus customers who are trying it now from car industry to logistics warehouse to, you know, to wind turbine plants. And So I'm really excited about the progress there. It represents probably the single greatest container of all of NVIDIA's technology, computer graphics, artificial intelligence, robotics, and physics simulation all into 1. I have great hopes for it.

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How Nvidia Grew From Gaming To A.I. Giant, Now Powering ChatGPT