I listen to, or read the transcripts of, podcasts regularly. One of my favourite podcast episodes this year was Jensen Huang’s appearance earlier this month in an episode of the Acquired FM podcast hosted by Ben Gilbert and David Rosenthal. Huang is the co-founder and CEO of Nvidia, a chip designer with US$32.7 billion in trailing revenue that’s in the epicenter of the AI revolution today. During his 1.5 hour interview with Gilbert and Rosenthal, Huang shared many pieces of wisdom – the passages below in italics are my favourites.
On how he sped up Nvidia’s chip development process by simulating the future
Jensen: We also made the decision to use this technology called emulation. There was a company called ICOS. On the day that I called them, they were just shutting the company down because they had no customers. I said, hey, look. I’ll buy what you have inventory. No promises are necessary.
The reason why we needed that emulator is because if you figure out how much money that we have, if we taped out a chip and we got it back from the fab and we started working on our software, by the time that we found all the bugs because we did the software, then we taped out the chip again. We would’ve been out of business already.
David: And your competitors would’ve caught up.
Jensen: Well, not to mention we would’ve been out of business.
David: Who cares?
Jensen: Exactly. If you’re going to be out of business anyway, that plan obviously wasn’t the plan. The plan that companies normally go through—build a chip, write the software, fix the bugs, tape out a new chip, so on and so forth—that method wasn’t going to work. The question is, if we only had six months and you get to tape out just one time, then obviously you’re going to tape out a perfect chip.
I remember having a conversation with our leaders and they said, but Jensen, how do you know it’s going to be perfect? I said, I know it’s going to be perfect, because if it’s not, we’ll be out of business. So let’s make it perfect. We get one shot.
We essentially virtually prototyped the chip by buying this emulator. Dwight and the software team wrote our software, the entire stack, ran it on this emulator, and just sat in the lab waiting for Windows to paint.
David: It was like 60 seconds for a frame or something like that.
Jensen: Oh, easily. I actually think that it was an hour per frame, something like that. We would just sit there and watch it paint. On the day that we decided to tape out, I assumed that the chip was perfect. Everything that we could have tested, we tested in advance, and told everybody this is it. We’re going to tape out the chip. It’s going to be perfect.
Well, if you’re going to tape out a chip and you know it’s perfect, then what else would you do? That’s actually a good question. If you knew that you hit enter, you tape out a chip, and you knew it was going to be perfect, then what else would you do? Well, the answer, obviously, go to production.
Ben: And marketing blitz. And developer relations.
Jensen: Kick everything off because you got a perfect chip. We got in our head that we have a perfect chip.
David: How much of this was you and how much of this was your co-founders, the rest of the company, the board? Was everybody telling you you were crazy?
Jensen: No. Everybody was clear we had no shot. Not doing it would be crazy.
David: Otherwise, you might as well go home.
Jensen: Yeah, you’re going to be out of business anyway, so anything aside from that is crazy. It seemed like a fairly logical thing. Quite frankly, right now as I’m describing it, you’re probably thinking yeah, it’s pretty sensible.
David: Well, it worked.
Jensen: Yeah, so we taped that out and went directly to production.
Ben: So is the lesson for founders out there when you have conviction on something like the RIVA 128 or CUDA, go bet the company on it. This keeps working for you. It seems like your lesson learned from this is yes, keep pushing all the chips in because so far it’s worked every time. How do you think about that?
Jensen: No, no. When you push your chips in I know it’s going to work. Notice we assumed that we taped out a perfect chip. The reason why we taped out a perfect chip is because we emulated the whole chip before we taped it out. We developed the entire software stack. We ran QA on all the drivers and all the software. We ran all the games we had. We ran every VGA application we had.
When you push your chips in, what you’re really doing is, when you bet the farm you’re saying, I’m going to take everything in the future, all the risky things, and I pull in in advance. That is probably the lesson. To this day, everything that we can prefetch, everything in the future that we can simulate today, we prefetch it.
On Nvidia’s corporate culture and architecture and why it works
Ben: We have some questions we want to ask you. Some are cultural about Nvidia, but others are generalizable to company-building broadly. The first one that we wanted to ask is that we’ve heard that you have 40+ direct reports, and that this org chart works a lot differently than a traditional company org chart.
Do you think there’s something special about Nvidia that makes you able to have so many direct reports, not worry about coddling or focusing on career growth of your executives, and you’re like, no, you’re just here to do your fricking best work and the most important thing in the world. Now go. (a) Is that correct? and (b) is there something special about Nvidia that enables that?
Jensen: I don’t think it’s something special in Nvidia. I think that we had the courage to build a system like this. Nvidia’s not built like a military. It’s not built like the armed forces, where you have generals and colonels. We’re not set up like that. We’re not set up in a command and control and information distribution system from the top down.
We’re really built much more like a computing stack. The lowest layer is our architecture, then there’s our chip, then there’s our software, and on top of it there are all these different modules. Each one of these layers of modules are people.
The architecture of the company (to me) is a computer with a computing stack, with people managing different parts of the system. Who reports to whom, your title is not related to anywhere you are in the stack. It just happens to be who is the best at running that module on that function on that layer, is in-charge. That person is the pilot in command. That’s one characteristic.
David: Have you always thought about the company this way, even from the earliest days?
Jensen: Yeah, pretty much. The reason for that is because your organization should be the architecture of the machinery of building the product. That’s what a company is. And yet, everybody’s company looks exactly the same, but they all build different things. How does that make any sense? Do you see what I’m saying?
How you make fried chicken versus how you flip burgers versus how you make Chinese fried rice is different. Why would the machinery, why would the process be exactly the same?
It’s not sensible to me that if you look at the org charts of most companies, it all looks like this. Then you have one group that’s for a business, and you have another for another business, you have another for another business, and they’re all supposedly autonomous.
None of that stuff makes any sense to me. It just depends on what is it that we’re trying to build and what is the architecture of the company that best suits to go build it? That’s number one.
In terms of information systems and how you enable collaboration, we’re wired up like a neural network. The way that we say this is that there’s a phrase in the company called ‘mission is the boss.’ We figure out what is the mission of what is the mission, and we go wire up the best skills, the best teams, and the best resources to achieve that mission. It cuts across the entire organization in a way that doesn’t make any sense, but it looks a little bit like a neural network.
David: And when you say mission, do you mean Nvidia’s mission is…
Jensen: Build Hopper.
David: Okay, so it’s not like further accelerated computing? It’s like we’re shipping DGX Cloud.
Jensen: No. Build Hopper or somebody else’s build a system for Hopper. Somebody has built CUDA for Hopper. Somebody’s job is to build cuDNN for CUDA for Hopper. Somebody’s job is the mission. Your mission is to do something.
Ben: What are the trade-offs associated with that versus the traditional structure?
Jensen: The downside is the pressure on the leaders is fairly high. The reason for that is because in a command and control system, the person who you report to has more power than you. The reason why they have more power than you is because they’re closer to the source of information than you are.
In our company, the information is disseminated fairly quickly to a lot of different people. It’s usually at a team level. For example, just now I was in our robotics meeting. We’re talking about certain things and we’re making some decisions.
There are new college grads in the room. There are three vice-presidents in the room, there are two e-staff in the room. At the moment that we decided together, we reasoned through some stuff, we made a decision, everybody heard it exactly the same time. Nobody has more power than anybody else. Does that make sense? The new college grad learned at exactly the same time as the e-staff.
The executive staff, the leaders that work for me, and myself, you earned the right to have your job based on your ability to reason through problems and help other people succeed. It’s not because you have some privileged information that I knew the answer was 3.7, and only I knew. Everybody knew.
On the right way to learn from business books
Jensen: In the last 30 years I’ve read my fair share of business books. As in everything you read, you’re supposed to first of all enjoy it, be inspired by it, but not to adopt it. That’s not the whole point of these books. The whole point of these books is to share their experiences.
You’re supposed to ask, what does it mean to me in my world, and what does it mean to me in the context of what I’m going through? What does this mean to me and the environment that I’m in? What does this mean to me in what I’m trying to achieve? What does this mean to Nvidia and the age of our company and the capability of our company?
You’re supposed to ask yourself, what does it mean to you? From that point, being informed by all these different things that we’re learning, we’re supposed to come up with our own strategies.
What I just described is how I go about everything. You’re supposed to be inspired and learn from everybody else. The education’s free. When somebody talks about a new product, you’re supposed to go listen to it. You’re not supposed to ignore it. You’re supposed to go learn from it.
It could be a competitor, it could be an adjacent industry, it could be nothing to do with us. The more we learn from what’s happening out in the world, the better. But then, you’re supposed to come back and ask yourself, what does this mean to us?
David: You don’t just want to imitate them.
Jensen: That’s right.
On the job of the CEO in a company
Jensen: That’s right. You want to pave the way to future opportunities. You can’t wait until the opportunity is sitting in front of you for you to reach out for it, so you have to anticipate.
Our job as CEO is to look around corners and to anticipate where will opportunities be someday. Even if I’m not exactly sure what and when, how do I position the company to be near it, to be just standing near under the tree, and we can do a diving catch when the apple falls. You guys know what I’m saying? But you’ve got to be close enough to do the diving catch.
On seeing the future of computing and AI before others did
Ben: Speaking of the speed of light—David’s begging me to go here—you totally saw that InfiniBand would be way more useful way sooner than anyone else realized. Acquiring Mellanox, I think you uniquely saw that this was required to train large language models, and you were super aggressive in acquiring that company. Why did you see that when no one else saw that?
Jensen: There were several reasons for that. First, if you want to be a data center company, building the processing chip isn’t the way to do it. A data center is distinguished from a desktop computer versus a cell phone, not by the processor in it.
A desktop computer in a data center uses the same CPUs, uses the same GPUs, apparently. Very close. It’s not the processing chip that describes it, but it’s the networking of it, it’s the infrastructure of it. It’s how the computing is distributed, how security is provided, how networking is done, and so on and so forth. Those characteristics are associated with Melanox, not Nvidia.
The day that I concluded that really Nvidia wants to build computers of the future, and computers of the future are going to be data centers, embodied in data centers, then if we want to be a data center–oriented company, then we really need to get into networking. That was one.
The second thing is observation that, whereas cloud computing started in hyperscale, which is about taking commodity components, a lot of users, and virtualizing many users on top of one computer, AI is really about distributed computing, where one training job is orchestrated across millions of processors.
It’s the inverse of hyperscale, almost. The way that you design a hyperscale computer with off-the-shelf commodity ethernet, which is just fine for Hadoop, it’s just fine for search queries, it’s just fine for all of those things—
Ben: But not when you’re sharding a model across.
Jensen: Not when you’re sharding a model across, right. That observation says that the type of networking you want to do is not exactly ethernet. The way that we do networking for supercomputing is really quite ideal.
The combination of those two ideas convinced me that Mellanox is absolutely the right company, because they’re the world’s leading high-performance networking company. We worked with them in so many different areas in high performance computing already. Plus, I really like the people. The Israel team is world class. We have some 3200 people there now, and it was one of the best strategic decisions I’ve ever made.
David: When we were researching, particularly part three of our Nvidia series, we talked to a lot of people. Many people told us the Mellanox acquisition is one of, if not the best of all time by any technology company.
Jensen: I think so, too. It’s so disconnected from the work that we normally do, it was surprising to everybody.
Ben: But framed this way, you were standing near where the action was, so you could figure out as soon as that apple becomes available to purchase, like, oh, LLMs are about to blow up, I’m going to need that. Everyone’s going to need that. I think I know that before anyone else does.
Jensen: You want to position yourself near opportunities. You don’t have to be that perfect. You want to position yourself near the tree. Even if you don’t catch the apple before it hits the ground, so long as you’re the first one to pick it up. You want to position yourself close to the opportunities.
That’s kind of a lot of my work, is positioning the company near opportunities, and the company having the skills to monetize each one of the steps along the way so that we can be sustainable.
On why zero-billion dollar markets are better than $10 billion markets
David: I’ve heard you or others in Nvidia (I think) used the phrase zero billion dollar—
Jensen: That’s exactly right. It’s our way of saying there’s no market yet, but we believe there will be one. Usually when you’re positioned there, everybody’s trying to figure out why are you here. When we first got into automotive, because we believe that in the future, the car is going to be largely software. If it’s going to be largely software, a really incredible computer is necessary.
When we positioned ourselves there, I still remember one of the CTOs told me, you know what? Cars cannot tolerate the blue screen of death. I said, I don’t think anybody can tolerate that, but that doesn’t change the fact that someday every car will be a software-defined car. I think 15 years later we’re largely right.
Oftentimes there’s non-consumption, and we like to navigate our company there. By doing that, by the time that the market emerges, it’s very likely there aren’t that many competitors shaped that way.
We were early in PC gaming, and today Nvidia’s very large in PC gaming. We reimagined what a design workstation would be like. Today, just about every workstation on the planet uses Nvidia’s technology. We reimagine how supercomputing ought to be done and who should benefit from supercomputing, that we would democratize it. And look today, Nvidia’s in accelerated computing is quite large.
We reimagine how software would be done, and today it’s called machine learning, and how computing would be done, we call it AI. We reimagined these things, try to do that about a decade in advance. We spent about a decade in zero billion dollar markets, and today I spent a lot of time on omniverse. Omniverse is a classic example of a zero billion dollar business.
Ben: There are like 40 customers now? Something like that?
David: Amazon, BMW.
Jensen: Yeah, I know. It’s cool.
On protecting a company’s moat (or competitive advantage)
Jensen: Oftentimes, if you created the market, you ended up having what people describe as moats, because if you build your product right and it’s enabled an entire ecosystem around you to help serve that end market, you’ve essentially created a platform.
Sometimes it’s a product-based platform. Sometimes it’s a service-based platform. Sometimes it’s a technology-based platform. But if you were early there and you were mindful about helping the ecosystem succeed with you, you ended up having this network of networks, and all these developers and customers who are built around you. That network is essentially your moat.
I don’t love thinking about it in the context of a moat. The reason for that is because you’re now focused on building stuff around your castle. I tend to like thinking about things in the context of building a network. That network is about enabling other people to enjoy the success of the final market. That you’re not the only company that enjoys it, but you’re enjoying it with a whole bunch of other people.
On the importance of luck in a company’s success
David: Is it fair to say, though, maybe on the luck side of the equation, thinking back to 1997, that that was the moment where consumers tipped to really, really valuing 3D graphical performance in games?
Jensen: Oh yeah. For example, luck. Let’s talk about luck. If Carmack had decided to use acceleration, because remember, Doom was completely software-rendered.
The Nvidia philosophy was that although general-purpose computing is a fabulous thing and it’s going to enable software and IT and everything, we felt that there were applications that wouldn’t be possible or it would be costly if it wasn’t accelerated. It should be accelerated. 3D graphics was one of them, but it wasn’t the only one. It just happens to be the first one and a really great one.
I still remember the first times we met John. He was quite emphatic about using CPUs and his software render was really good. Quite frankly, if you look at Doom, the performance of Doom was really hard to achieve even with accelerators at the time. If you didn’t have to do bilinear filtering, it did a pretty good job.
David: The problem with Doom, though, was you needed Carmac to program it.
Jensen: Exactly. It was a genius piece of code, but nonetheless, software renders did a really good job. If he hadn’t decided to go to OpenGL and accelerate for Quake, frankly what would be the killer app that put us here? Carmack and Sweeney, both between Unreal and Quake, created the first two killer applications for consumer 3D, so I owe them a great deal.
On the importance of having an ecosystem of 3rd-party developers surrounding your company
David: I want to come back real quick to you told these stories and you’re like, well, I don’t know what founders can take from that. I actually do think if you look at all the big tech companies today, perhaps with the exception of Google, they did all start—and understanding this now about you—by addressing developers, planning to build a platform, and tools for developers.
All of them—Apple, not Amazon. […] That’s how AWS started. I think that actually is a lesson to your point of, that won’t guarantee success by any means, but that’ll get you hanging around a tree if the apple falls.
Jensen: As many good ideas as we have. You don’t have all the world’s good ideas and the benefit of having developers is you get to see a lot of good ideas.
On keeping AI safe, and how AI can change the world for the better
Ben: I want to think about the future a little bit. I’m sure you spend a lot of time on this being on the cutting edge of AI.
We’re moving into an era where the productivity that software can accomplish when a person is using software can massively amplify the impact and the value that they’re creating, which has to be amazing for humanity in the long run. In the short term, it’s going to be inevitably bumpy as we figure out what that means.
What do you think some of the solutions are as AI gets more and more powerful and better at accelerating productivity for all the displaced jobs that are going to come from it?
Jensen: First of all, we have to keep AI safe. There are a couple of different areas of AI safety that’s really important. Obviously, in robotics and self-driving car, there’s a whole field of AI safety. We’ve dedicated ourselves to functional and active safety, and all kinds of different areas of safety. When to apply human in the loop? When is it okay for a human not to be in the loop? How do you get to a point where increasingly human doesn’t have to be in the loop, but human largely in the loop?
In the case of information safety, obviously bias, false information, and appreciating the rights of artists and creators, that whole area deserves a lot of attention.
You’ve seen some of the work that we’ve done, instead of scraping the Internet we, we partnered with Getty and Shutterstock to create commercially fair way of applying artificial intelligence, generative AI.
In the area of large language models in the future of increasingly greater agency AI, clearly the answer is for as long as it’s sensible—and I think it’s going to be sensible for a long time—is human in the loop. The ability for an AI to self-learn, improve, and change out in the wild in a digital form should be avoided. We should collect data. We should carry the data. We should train the model. We should test the model, validate the model before we release it in the wild again. So human is in the loop.
There are a lot of different industries that have already demonstrated how to build systems that are safe and good for humanity. Obviously, the way autopilot works for a plane, two-pilot system, then air traffic control, redundancy and diversity, and all of the basic philosophies of designing safe systems apply as well in self-driving cars, and so on and so forth. I think there are a lot of models of creating safe AI, and I think we need to apply them.
With respect to automation, my feeling is that—and we’ll see—it is more likely that AI is going to create more jobs in the near term. The question is what’s the definition of near term? And the reason for that is the first thing that happens with productivity is prosperity. When the companies get more successful, they hire more people because they want to expand into more areas.
So the question is, if you think about a company and say, okay, if we improve the productivity, then need fewer people. Well, that’s because the company has no more ideas. But that’s not true for most companies. If you become more productive and the company becomes more profitable, usually they hire more people to expand into new areas.
So long as we believe that they’re more areas to expand into, there are more ideas in drugs, there’s drug discovery, there are more ideas in transportation, there are more ideas in retail, there are more ideas in entertainment, that there are more ideas in technology, so long as we believe that there are more ideas, the prosperity of the industry which comes from improved productivity, results in hiring more people, more ideas.
Now you go back in history. We can fairly say that today’s industry is larger than the world’s industry a thousand years ago. The reason for that is because obviously, humans have a lot of ideas. I think that there are plenty of ideas yet for prosperity and plenty of ideas that can be begat from productivity improvements, but my sense is that it’s likely to generate jobs.
Now obviously, net generation of jobs doesn’t guarantee that any one human doesn’t get fired. That’s obviously true. It’s more likely that someone will lose a job to someone else, some other human that uses an AI. Not likely to an AI, but to some other human that uses an AI.
I think the first thing that everybody should do is learn how to use AI, so that they can augment their own productivity. Every company should augment their own productivity to be more productive, so that they can have more prosperity, hire more people.
I think jobs will change. My guess is that we’ll actually have higher employment, we’ll create more jobs. I think industries will be more productive. Many of the industries that are currently suffering from lack of labor, workforce is likely to use AI to get themselves off their feet and get back to growth and prosperity. I see it a little bit differently, but I do think that jobs will be affected, and I’d encourage everybody just to learn AI.
David: This is appropriate. There’s a version of something we talked about a lot on Acquired, we call it the Moritz corollary to Moore’s law, after Mike Moritz from Sequoia.
Jensen: Sequoia was the first investor in our company.
David: Of course, yeah. The great story behind it is that when Mike was taking over for Don Valentine with Doug, he was sitting and looking at Sequoia’s returns. He was looking at fund three or four, I think it was four maybe that had Cisco in it. He was like, how are we ever going to top that? Don’s going to have us beat. We’re never going to beat that.
He thought about it and he realized that, well, as compute gets cheaper, and it can access more areas of the economy because it gets cheaper, and can it get adopted more widely, well then the markets that we can address should get bigger. Your argument is basically AI will do the same thing. The cycle will continue.
Jensen: Exactly. I just gave you exactly the same example that in fact, productivity doesn’t result in us doing less. Productivity usually results in us doing more. Everything we do will be easier, but we’ll end up doing more. Because we have infinite ambition. The world has infinite ambition. If a company is more profitable, they tend to hire more people to do more.
On the importance of prioritising your daily activities
David: What is something that you believe today that 40-year-old Jensen would’ve pushed back on and said, no, I disagree.
Jensen: There’s plenty of time. If you prioritize yourself properly and you make sure that you don’t let Outlook be the controller of your time, there’s plenty of time.
David: Plenty of time in the day? Plenty of time to achieve this thing?
Jensen: To do anything. Just don’t do everything. Prioritize your life. Make sacrifices. Don’t let Outlook control what you do every day.
Notice I was late to our meeting, and the reason for that, by the time I looked up, oh my gosh. Ben and David are waiting.
David: We have time.
Jensen: Exactly.
David: Didn’t stop this from being your day job.
Jensen: No, but you have to prioritize your time really carefully, and don’t let Outlook determine that.
On what is the really important thing in a business plan: The problem you want to solve
Jensen: I didn’t know how to write a business plan.
Ben: Which it turns out is not actually important.
Jensen: No. It turns out that making a financial forecast that nobody knows is going to be right or wrong, turns out not to be that important. But the important things that a business plan probably could have teased out, I think that the art of writing a business plan ought to be much, much shorter.
It forces you to condense what is the true problem you’re trying to solve? What is the unmet need that you believe will emerge? And what is it that you’re going to do that is sufficiently hard, that when everybody else finds out is a good idea, they’re not going to swarm it and make you obsolete? It has to be sufficiently hard to do.
There are a whole bunch of other skills that are involved in just product positioning, pricing, go to market and all that stuff. But those are skills, and you can learn those things easily. The stuff that is really, really hard is the essence of what I described.
I did that okay, but I had no idea how to write the business plan. I was fortunate that Wilf Corrigan was so pleased with me in the work that I did when I was at LSI Logic, he called up Don Valentine and told Don, invest in this kid. He’s going to come your way. I was set up for success from that moment and got us off the ground.
On entrepreneurs’ superpower
David: Well, and that being our final question for you. It’s 2023, 30 years anniversary of the founding of Nvidia. If you were magically 30 years old again today in 2023, and you were going to Denny’s with your two best friends who are the two smartest people you know, and you’re talking about starting a company, what are you talking about starting?
Jensen: I wouldn’t do it. I know. The reason for that is really quite simple. Ignoring the company that we would start, first of all, I’m not exactly sure. The reason why I wouldn’t do it, and it goes back to why it’s so hard, is building a company and building Nvidia turned out to have been a million times harder than I expected it to be, any of us expected it to be.
At that time, if we realized the pain and suffering, just how vulnerable you’re going to feel, and the challenges that you’re going to endure, the embarrassment and the shame, and the list of all the things that go wrong, I don’t think anybody would start a company. Nobody in their right mind would do it.
I think that that’s the superpower of an entrepreneur. They don’t know how hard it is, and they only ask themselves how hard can it be? To this day, I trick my brain into thinking, how hard can it be? Because you have to.
On the importance of self-belief
David: I know how meaningful that is in any company, but for you, I feel like the Nvidia journey is particularly amplified on these dimensions. You went through two, if not three, 80%-plus drawdowns in the public markets, and to have investors who’ve stuck with you from day one through that, must be just so much support.
Jensen: It is incredible. You hate that any of that stuff happened. Most of it is out of your control, but 80% fall, it’s an extraordinary thing no matter how you look at it.
I forget exactly, but we traded down at about a couple of $2–$3 billion in market value for a while because of the decision we made in going into CUDA and all that work. Your belief system has to be really, really strong. You have to really, really believe it and really, really want it.
Otherwise, it’s just too much to endure because everybody’s questioning you. Employees aren’t questioning you, but employees have questions. People outside are questioning you, and it’s a little embarrassing.
It’s like when your stock price gets hit, it’s embarrassing no matter how you think about it. It’s hard to explain. There are no good answers to any of that stuff. The CEOs are humans and companies are built of humans. These challenges are hard to endure.
On how technology transforms and grows economic opportunities
Jensen: This is the extraordinary thing about technology right now. Technology is a tool and it’s only so large. What’s unique about our current circumstance today is that we’re in the manufacturing of intelligence. We’re in the manufacturing of work world. That’s AI. The world of tasks doing work—productive, generative AI work, generative intelligent work—that market size is enormous. It’s measured in trillions.
One way to think about that is if you built a chip for a car, how many cars are there and how many chips would they consume? That’s one way to think about that. However, if you build a system that, whenever needed, assisted in the driving of the car, what’s the value of an autonomous chauffeur every now and then?
Obviously, the problem becomes much larger, the opportunity becomes larger. What would it be like if we were to magically conjure up a chauffeur for everybody who has a car, and how big is that market? Obviously, that’s a much, much larger market.
The technology industry is that what we discovered, what Nvidia has discovered, and what some of the discovered, is that by separating ourselves from being a chip company but building on top of a chip and you’re now an AI company, the market opportunity has grown by probably a thousand times.
Don’t be surprised if technology companies become much larger in the future because what you produce is something very different. That’s the way to think about how large can your opportunity, how large can you be? It has everything to do with the size of the opportunity.
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