NVIDIA CEO Jensen Huang traces his journey from a 9-year-old immigrant cleaning toilets in Kentucky to revolutionizing AI with chips like the DGX Spark, now priced at $4,000 after a decade of 100,000x performance gains. His near-bankruptcy pivot in 1995—securing a $5M Sega investment despite technical failures—mirrors NVIDIA’s 2012 AI bet, delivering the first DGX-1 to OpenAI despite skepticism. Huang dismisses AI doomsday fears, calling it a "universal function approximator" lacking true consciousness, and predicts energy-efficient models will soon bridge global tech divides. Success, he insists, isn’t just joy—it’s resilience through suffering, loneliness, and humiliation, lessons from building gaming GPUs to powering modern AI. [Automatically generated summary]
But I like the fact that he's telling you what's on his mind.
Almost every time he explains something and he says something, he starts with his, you could tell his love for America, what he wants to do for America.
And everything that he thinks through is very practical and very common sense.
And, you know, it's very logical.
And I still remember the first time I met him.
And so this was, I'd never known him, never met him before.
And Secretary Lutnick called, and we met right at the beginning of the administration.
And he said, he told me what was important to President Trump, that United States manufactures on shore.
And that was really important to him because it's important to national security.
He wants to make sure that the important critical technology of our nation is built in the United States and that we reindustrialize and get good at manufacturing again because it's important for jobs.
It's just unfortunate we live in such a politically polarized society that you can't recognize good common sense things if they're coming from a person that you object to.
And that, I think, is what's going on here.
I think most people generally, as a country, you know, as a giant community, which we are, it just only makes sense that we have manufacturing in America, especially critical technology like you're talking about.
Like, it's kind of insane that we buy so much technology from other countries.
Now, when we're talking about technology growth and energy growth, there's a lot of people that go, oh, no, that's not what we need.
We need to simplify our lives and get back.
But the real issue is that we're in the middle of a giant technology race.
And whether people are aware of it or not, whether they like it or not, it's happening.
And it's a really important race because whoever gets to whatever the event horizon of artificial intelligence is, whoever gets there first, has massive advantages in a huge way.
Or, you know, even going back to the discovery of energy, right?
The United Kingdom was where the Industrial Revolution was, if you will, invented, when they realized that they can turn steam and such into energy, into electricity.
All of that was invented largely in Europe.
And the United States capitalized on it.
We were the ones that learned from it.
We industrialized it.
We diffused it faster than anybody in Europe.
They were all stuck in discussions about policy and jobs and disruptions.
Meanwhile, the United States was forming.
We just took the technology and ran with it.
And so I think we were always in a bit of a technology race.
World War II was a technology race.
Manhattan Project was a technology race.
We've been in the technology race ever since during the Cold War.
I think we're still in a technology race.
It is probably the single most important race.
It is the technology gives you superpowers, you know, whether it's information superpowers or energy superpowers or military superpowers is all founded in technology.
It seems like with the AI race, people are very nervous about it.
Like, you know, Elon has famously said that it's like 80% chance it's awesome.
20% chance we're in trouble.
And people are worried about that 20%, rightly so.
I mean, you know, if you had 10 bullets in a revolver and you took out eight of them, you still have two in there and you spin it, you're not going to feel real comfortable when you pull that trigger.
And when we're working towards this ultimate goal of AI, it's impossible to imagine that it wouldn't be of national security interest to get there first.
I think it was better, and more horsepower is better.
My 459 is better than my 430.
More horsepower is better.
I think more horsepower is better.
I think it's better handling, it's better control.
In the case of technology, it's also very similar in that way.
And so if you look at what we're going to do with the next thousand times of performance in AI, a lot of it is going to be channeled towards more reflection, more research, thinking about the answer more deeply.
And then you take the technology and the horsepower, you put guardrails on it, just like our cars.
We've got a lot of technology in a car today.
A lot of it goes towards, for example, ABS.
ABS is great.
And so traction control.
That's fantastic.
Without a computer in the car, how would you do any of that?
And that little computer, the computers that you have doing your traction control, is more powerful than the computer that went to Apollo 11.
And so you want that technology, channel it towards safety, channel it towards functionality.
And so when people talk about power, the advancement of technology, oftentimes I feel what they're thinking and what we're actually doing is very different.
Well, they're thinking somehow that this AI is being powerful, and their mind probably goes towards a sci-fi movie, the definition of power.
Oftentimes, the definition of power is military power or physical power.
But in the case of technology power, when we translate all of those operations, it's towards more refined thinking, more reflection, more planning, more options.
I think the big fears that people have is one, a big fear is military applications.
That's a big fear.
Because people are very concerned that you're going to have AI systems that make decisions that maybe an ethical person wouldn't make or a moral person wouldn't make based on achieving an objective versus based on how it's going to look to people.
And I think it's happy that we're making it more socially acceptable.
There was a time where when somebody wanted to channel their technology capability and their intellect into defense technology, somehow they're vilified.
But we need people like that.
We need people who enjoyed that part of application of technology.
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That's a big issue with people: the worry that technology is going to get to a point where encryption is going to be obsolete.
Encryption is just, it's no longer going to protect data, it's no longer going to protect systems.
Do you anticipate that ever being an issue, or do you think it's as the defense grows, the threat grows, then defense grows, and it just keeps going on and on and on, and they'll always be able to fight off any sort of intrusions?
The crazy thing is when you hear about the kind of computation that quantum computing can do and the power that it has, where you're looking at all the supercomputers in the world, it would take billions of years and it takes them a few minutes to solve these equations.
How do you make encryption for something that can do that?
My worry is this is a totally, you know, it's like history was one thing and then nuclear weapons kind of changed all of our thoughts on war and mutually assured destruction came or got everybody to stop using nuclear bombs.
But the thing is, Joe, is that AI is not going to, it's not like we're cavemen and then all of a sudden one day AI shows up.
Every single day, we're getting better and smarter because we have AI.
And so we're stepping on our own AI's shoulders.
So when that, whatever that AI threat comes, it's a click ahead.
It's not a galaxy ahead.
You know, it's just a click ahead.
And so I think the idea that somehow this AI is going to pop out of nowhere and somehow think in a way that we can't even imagine thinking and do something that we can't possibly imagine, I think is far-fetched.
And the reason for that is because we all have AIs, and there's a whole bunch of AIs being in development.
We know what they are, and we're using it.
And so every single day, we're close to each other.
Okay, let's just pretend for a second that we believe that.
I don't believe, actually, I actually don't believe that.
But nonetheless, let's pretend we believe that.
So your AI is conscious, and my AI is conscious.
And let's say your AI is, you know, wants to, I don't know, do something surprising.
My AI is so smart that it might be surprising to me, but it probably won't be surprising to my AI.
And so maybe my AI thinks it's surprising as well.
But it's so smart, the moment it sees it the first time, it's not going to be surprised the second time, just like us.
And so I feel like I think the idea that only one person has AI and that one person's AI compares to everybody else's AI as Neanderthal is probably unlikely.
The consciousness, I guess first of all, you need to know about your own existence.
You have to have experience, not just knowledge and intelligence.
The concept of a machine having an experience, I'm not, well, first of all, I don't know what defines experience, why we have experiences and why this microphone doesn't.
And so I think I know, well, I think I know what consciousness is.
The sense of experience, the ability to know self versus the ability to be able to reflect, know our own self, the sense of ego.
I think all of those human experiences probably is what consciousness is.
But why it exists versus the concept of knowledge and intelligence, which is what AI is defined by today.
It has knowledge, it has intelligence, artificial intelligence.
We don't call it artificial consciousness.
Artificial intelligence, the ability to perceive, recognize, understand, plan, perform tasks.
Those things are foundations of intelligence to know things.
Knowledge.
I don't, it's clearly different than consciousness.
You're aware that AI, I forget which one, where they gave it some false information about one of the programmers having an affair with his wife just to see how it would respond to it.
And then when they said they were going to shut it down, it threatened to blackmail him and reveal his affair.
And it was like, whoa, like it's conniving.
Like if that's not learning from experience and being aware that you're about to be shut down, which would imply at least some kind of consciousness, or you could kind of define it as consciousness if you were very loose with the term.
And if you imagine that this is going to exponentially become more powerful, wouldn't that ultimately lead to a different kind of consciousness than we're defining from biology?
That in the collection of numbers that relates to a husband cheating on a wife has subsequently a bunch of numbers that relates to black male and such things, whatever the revenge was.
But at a certain point in time, one would say, okay, well, it couldn't do this two years ago, and it couldn't do this four years ago.
Like, when we were looking towards the future, like, at what point in time, when it can do everything a person does, what point in time do we decide that it's conscious?
If it absolutely mimics all human thinking and behavior patterns, that doesn't make it conscious.
It becomes indiscernible.
It's aware, it can communicate with you the exact same way a person can.
Like, is consciousness, are we putting too much weight on that concept?
Because it seems like it's a version of a kind of consciousness.
I believe it is possible to create a machine that imitates human intelligence and has the ability to understand information, understand instructions, break the problem down, solve problems, and perform tasks.
I believe that completely.
I believe that we could have a computer that has a vast amount of knowledge, some of it true, some of it not true, some of it generated by humans, some of it generated synthetically, and more and more of knowledge in the world will be generated synthetically going forward.
Until now, the knowledge that we have are knowledge that we generate and we propagate and we send to each other and we amplify it and we add to it and we modify it, we change it.
In the future, in a couple of years, maybe two or three years, 90% of the world's knowledge will likely be generated by AI.
It's because what difference does it make to me that I am learning from a textbook that was generated by a bunch of people I didn't know or written by a book that, you know, from somebody I don't know, to knowledge generated by AI, computers that are assimilating all of these and re-synthesizing things.
To me, I don't think there's a whole lot of difference.
We still have to fact-check it.
We still have to make sure that it's based on fundamental first principles.
And we still have to do all of that, just like we do today.
Is this taking into account the kind of AI that exists currently?
And do you anticipate that just like we could have never really believed that AI would be, at least a person like myself would never believe AI would be as so ubiquitous and so worth it's so powerful today and so important today.
We never thought that 10 years ago.
Never thought that.
Imagine like what are we looking at 10 years from now?
One of the things that Elon said that makes me happy is He believes that we're going to get to a point where it's not necessary for people to work.
And not meaning that you're going to have no purpose in life, but you will have, in his words, universal high income because so much revenue is generated by AI that it will take away this need for people to do things that they don't really enjoy doing just for money.
And I think a lot of people have a problem with that because their entire identity and how they think of themselves and how they fit in the community is what they do.
Like, this is Mike, he's an amazing mechanic.
Go to Mike, and Mike takes care of things.
But there's going to come a point in time where AI is going to be able to do all those things much better than people do.
And people will just be able to receive money.
But then what does Mike do?
When Mike really loves being the best mechanic around.
What does the guy who codes, what does he do when AI can code infinitely faster with zero errors?
Like what happens with all those people?
And that is where it gets weird.
It's like, because we've sort of wrapped our identity as human beings around what we do for a living.
You know, when you meet someone, one of the first things you meet somebody at a party, hi, Joe, what's your name, Mike?
What do you do, Mike?
And, you know, Mike's like, oh, I'm a lawyer.
Oh, what kind of law?
And you have a conversation.
You know, when Mike is like, I get money from the government, I play video games.
It gets weird.
And I think the concept sounds great until you take into account human nature.
And human nature is that we like to have puzzles to solve and things to do and an identity that's wrapped around our idea that we're very good at this thing that we do for a living.
So one of the predictions from Jeff Hinton, who started the whole deep learning phenomenon, the deep learning technology trend, and incredible, incredible researcher, professor at University of Toronto.
He invented, discovered, or invented the idea of back propagation, which allows the neural network to learn.
And as you know, for the audience, software historically was humans applying first principles and our thinking to describe an algorithm that is then codified just like a recipe that's codified in software.
It looks just like a recipe, how to cook something.
It looks exactly the same, just in a slightly different language.
We call it Python or C or C or whatever it is.
In the case of deep learning, this invention of artificial intelligence, we put a structure of a whole bunch of neural networks and a whole bunch of math units.
And we make this large structure.
It's like a switchboard of little mathematical units.
And we connect it all together.
And we give it the input that the software would eventually receive.
And we just let it randomly guess what the output is.
And so we say, for example, the input could be a picture of a cat.
And one of the outputs of the switchboard is where the cat signal is supposed to show up.
And all of the other signals, the other one's a dog, the other one's an elephant, the other one's a tiger, and all of the other signals are supposed to be zero when I show it a cat.
And the one that is a cat should be one.
And I show it a cat through this big, huge network of switchboards and math units.
And they're just doing multiply and adds, multiplies and adds.
And this thing, this switchboard, is gigantic.
The more information you're going to give it, the more the bigger this switchboard has to be.
And what Jeff Hinton discovered was invented, was a way for you to guess that, put the cat signal in, put the cat image in, and that cat image, you know, could be a million numbers because it's a megapixel image, for example.
And it's just a whole bunch of numbers.
And somehow, from those numbers, it has to light up the cat signal.
Okay, that's the bottom line.
And the first time you do it, it just comes up with garbage.
And so it says the right answer is cat.
And so you need to increase this signal and decrease all of the other and backpropagates the outcome through the entire network.
And then you show it another, now it's an image of a dog, and it guesses it, takes a swing at it, and it comes up with a bunch of garbage.
And you say, no, no, no, the answer is this is a dog.
I want you to produce a dog.
And all of the other switch, all the other outputs have to be zero.
And I want to backpropagate that and just do it over and over and over again.
It's just like showing a kid this is an apple, this is a dog, this is a cat, and you just keep showing it to them until they eventually get it.
Okay, well, anyways, that big invention is deep learning.
That's the foundation of artificial intelligence.
A piece of software that learns from examples.
That's basically machine learning, a machine that learns.
And so one of the big first applications was image recognition.
And one of the most important image recognition applications is radiology.
And so he predicted about five years ago that in five years' time, the world won't need any radiologists because AI would have swept the whole field.
Well, it turns out AI has swept the whole field.
That is completely true.
Today, just about every radiologist is using AI in some way.
And what's ironic though, what's interesting is that the number of radiologists has actually grown.
And so the prediction was, in fact, that 30 million radiologists will be wiped out.
But as it turns out, we needed more.
And the reason for that is because the purpose of a radiologist is to diagnose disease, not to study the image.
The image studying is simply a task in service of diagnosing the disease.
And so now, the fact that you could study the images more quickly and more precisely without ever making a mistake and never gets tired.
You could study more images.
You could study it in 3D form instead of 2D because, you know, the AI doesn't care whether it studies images in 3D or 2D.
You could study it in 4D.
And so now you could study images in a way that radiologists can't easily do.
And you could study a lot more of it.
And so the number of tests that people are able to do increases.
And because they're able to serve more patients, the hospital does better.
They have more clients, more patients.
As a result, they have better economics.
When they have better economics, they hire more radiologists because their purpose is not to study the images.
Their purpose is to diagnose disease.
And so the question is, what I'm leading up to is, ultimately, what is the purpose?
What is the purpose of the lawyer?
And has the purpose changed?
What is the purpose?
You know, one of the examples that I gave that I would give is, for example, if my car became self-driving, will all chauffeurs be out of jobs?
The answer probably is not.
Because some chauffeurs, for some people who are driving you, they could be protectors.
Some people, they're part of the experience, part of the service.
So when you get there, they could take care of things for you.
And so for a lot of different reasons, not all chauffeurs would lose their jobs.
Some chauffeurs would lose their jobs.
And many chauffeurs would change their jobs.
And the type of applications of autonomous vehicles will probably increase.
The usage of the technology within find new homes.
And so I think you have to go back to what is the purpose of a job.
Like, for example, if AI comes along, I actually don't believe I'm going to lose my job because my purpose isn't to, I have to look at a lot of documents.
I study a lot of emails.
I look at a bunch of diagrams.
The question is, what is the job?
And the purpose of somebody probably hasn't changed.
A lawyer, for example, help people.
That probably hasn't changed.
Studying legal documents, generating documents, it's part of the job, not the job.
It could be a lot of people, but it'll probably generate – like, for example, let's say I'm super excited about the robots Elon's working on.
It's still a few years away.
When it happens, when it happens, there's a whole new industry of technicians and people who have to manufacture the robots, right?
And so that job never existed.
And so you're going to have a whole industry of people taking care of, like, for example, all the mechanics and all the people who are building things for cars, supercharging cars.
That didn't exist before cars, and now we're going to have robots.
You're going to have robot apparels.
So a whole industry of, right, isn't that right?
Because I want my robot to look different than your robot.
Oh, God.
And so you're going to have a whole apparel industry for robots.
You're going to have mechanics for robots.
And you have people who come to maintain your robots.
And so we're either going to be all wealthy or we're going to be all universal.
How could everybody be wealthy, though?
Wealthy, not because you have a lot of dollars, wealthy because there's a lot of abundance.
Like, for example, today, we are wealthy of information.
This is a concept several thousand years ago, only a few people have.
And so today we have wealth of a whole bunch of things, resources that – That's a good point.
Yeah.
And so we're going to have wealth of resources, things that we think are valuable today that in the future are just not that valuable.
And so it – Because it's automated.
And so I think the question, maybe partly, it's hard to answer, partly because it's hard to talk about infinity, and it's hard to talk about a long time from now.
And the reason for that is because there's just too many scenarios to consider.
But I think in the next several years, call it five to ten years, there are several things that I believe in hope.
And I say hope because I'm not sure.
One of the things that I believe is that the technology divide will be substantially collapsed.
And of course, the alternative viewpoint is that AI is going to increase the technology divide.
Now, the reason why I believe AI is going to reduce the technology divide is because we have proof.
The evidence is that AI is the easiest application in the world to use.
ChatGPT has grown to almost a billion users, frankly, practically overnight.
And if you're not exactly sure how to use, everybody knows how to use ChatGPT, just say something to it.
If you're not sure how to use ChatGPT, you ask ChatGPT how to use it.
No tool in history has ever had this capability.
AquisonArt.
If you don't know how to use it, you're kind of screwed.
You're going to walk up to it and say, how do you use a Cuisinart?
You're going to have to find somebody else.
But an AI will just tell you exactly how to do it.
Anybody could do this.
It'll speak to you in any language.
And if it doesn't know your language, you'll speak it in that language and it'll probably figure out that it doesn't completely understand your language, go learns it instantly and comes back and talk to you.
And so I think the technology divide has a real chance finally that you don't have to speak Python or C ⁇ or Fortran.
You can just speak human and whatever form of human you like.
And so I think that that has a real chance of closing the technology divide.
Now, of course, the counter narrative would say that AI is only going to be available for the nations and the countries that have a vast amount of resources because AI takes energy and AI takes a lot of GPUs and factories to be able to produce the AI.
No doubt at the scale that we would like to do in the United States.
But the fact of the matter is, your phone's going to run AI just fine, all by itself, you know, in a few years.
Today, it already does it fairly decently.
And so the fact that every country, every nation, every society will have to benefit of very good AI.
It might not be tomorrow's AI.
It might be yesterday's AI, but yesterday's AI is freaking amazing.
You know, in 10 years' time, nine-year-old AI is going to be amazing.
You don't need 10-year-old AI.
You don't need frontier AI like we need frontier AI because we want to be the world leader.
But for every single country, everybody, I think the capability to elevate everybody's knowledge and capability and intelligence, that day is coming.
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And also energy production, which is the real bottleneck when it comes to third world countries and electricity and all the resources that we take for granted.
Almost everything is going to be energy constrained.
And so if you take a look at one of the most important technology advances in history is this idea called Moore's Law.
Moore's Law started basically in my generation.
And my generation is the generation of computers.
I graduated in 1984, and that was basically at the very beginning of the PC revolution and the microprocessor.
And every single year, it approximately doubled.
And we describe it as every single year we double the performance.
But what it really means is that every single year, the cost of computing halved.
And so the cost of computing in the course of five years reduced by a factor of 10.
The amount of energy necessary to do computing, to do any task, reduced by a factor of 10.
Every single 10 years, 100, 1,000, 10,000, 100,000, so on and so forth.
And so each one of the clicks of Moore's Law, the amount of energy necessary to do any computing reduced.
That's the reason why you have a laptop today when back in 1984 it sat on the desk, you got a plug in, it wasn't that fast, and it consumed a lot of power.
Today, you know, it is only a few watts.
And so Moore's Law is the fundamental technology, the fundamental technology trend that made it possible.
Well, what's going on in AI?
The reason why NVIDIA is here is because we invented this new way of doing computing.
We call it accelerated computing.
We started it 33 years ago.
It took us about 30 years to really make a huge breakthrough.
In that 30 years or so, we took computing, you know, probably a factor of, well, let me just say in the last 10 years, the last 10 years, we improved the performance of computing by 100,000 times.
Imagine a car over the course of 10 years, it became 100,000 times faster.
Or at the same speed, 100,000 times cheaper.
Or at the same speed, 100,000 times less energy.
If your car did that, it doesn't need energy at all.
What I mean, what I'm trying to say is that in 10 years' time, the amount of energy necessary for artificial intelligence for most people will be minuscule, utterly minuscule.
And so we'll have AI running in all kinds of things and all the time because it doesn't consume that much energy.
And so if you're a nation that uses AI for, you know, almost everything in your social fabric, of course, you're going to need these AI factories.
But for a lot of countries, I think you're going to have excellent AI and you're not going to need as much energy.
Everybody will be able to come along, is my point.
And so in 2012, Jeff Hinton's lab, this gentleman I was talking about, Ilya Suskabur, Alex Krushzewski, they made a breakthrough in computer vision in literally creating a piece of software called AlexNet.
And its job was to recognize images.
And it recognized images at a level, computer vision, which is fundamental to intelligence.
If you can't perceive, it's hard to have intelligence.
And so computer vision is a fundamental pillar of, not the only, but fundamental pillar of.
And so breaking computer vision or breaking through in computer vision is pretty foundational to almost everything that everybody wants to do in AI.
And so in 2012, their lab in Toronto made this breakthrough called AlexNet.
And AlexNet was able to recognize images so much better than any human created computer vision algorithm.
in the 30 years prior.
So all of these people, all these scientists, and we had many too, working on computer vision algorithms.
And these two kids, Ilya and Alex, under Jeff Hinton, took a giant leap above it.
And it was based on this thing called ElexNet, this neural network.
And the way it ran, the way they made it work was literally buying two NVIDIA graphics cards.
Because NVIDIA's GPUs, we've been working on this new way of doing computing.
And our GPU's application, and it's basically a supercomputing application back in 1984, in order to process computer games and what you have in your racing simulator, that is called an image generator supercomputer.
And so NVIDIA started, our first application was computer graphics.
And we applied this new way of doing computing where we do things in parallel instead of sequentially.
A CPU does things sequentially.
Step one, step two, step three.
In our case, we break the problem down and we give it to thousands of processors.
And so our way of doing computation is much more complicated.
But if you're able to formulate the problem in the way that we create it called CUDA, this is the invention of our company, if you could formulate it in that way, we could process everything simultaneously.
Now, in the case of computer graphics, it's easier to do because every single pixel on your screen is not related to every other pixel.
And so I could render multiple parts of the screen at the same time.
Not completely true, because maybe the way lighting works or the way shadow works, there's a lot of dependency and such.
But computer graphics, with all the pixels, I should be able to process everything simultaneously.
And so we took this embarrassingly parallel problem called computer graphics and we applied it to this new way of doing computing, NVIDIA's accelerated computing.
We put it in all of our graphics cards.
Kids were buying it to play games.
You probably don't know this, but we're the largest gaming platform in the world today.
And so anyways, these two kids trained this model using the technique I described earlier on our GPUs because our GPUs could process things in parallel.
It's essentially a supercomputer in a PC.
The reason why you used it for Quake is because it is the first consumer supercomputer.
Okay?
And so anyways, they made that breakthrough.
We were working on computer vision at the time.
It caught my attention.
And so we went to learn about it.
Simultaneously, this deep learning phenomenon was happening all over the country.
Universities after another recognized the importance of deep learning, and all of this work was happening at Stanford, at Harvard, at Berkeley, just all over the place.
New York University, Yan Le Kun, Andrew Yang at Stanford, so many different places.
And I see it cropping up everywhere.
And so my curiosity asked, you know, what is so special about this form of machine learning?
And we've known about machine learning for a very long time.
We've known about AI for a very long time.
We've known about neural networks for a very long time.
What makes now the moment?
And so we realized that this architecture for deep neural networks, back propagation, the way deep neural networks were created, we could probably scale this problem, scale the solution to solve many problems.
That is essentially a universal function approximator.
Okay, meaning, you know, back when you're in school, you have a box, inside of it is a function.
You give it an input, it gives you an output.
And the reason why I call it a universal function approximator is that this computer, instead of you describing the function, a function could be a Newton's equation, F equals MA.
That's a function.
You write the function in software, you give it input, F, mass, acceleration, it'll tell you the force.
And the way this computer works is really interesting.
You give it a universal function.
It's not F equals MA, it's just a universal function.
It's a big, huge deep neural network.
And instead of describing the insight, you give it examples of input and output, and it figures out the inside.
So you give it input and output, and it figures out the inside.
A universal function approximator.
Today, it could be Newton's equation.
Tomorrow it could be Maxwell's equation.
It could be Coulomb's law.
It could be thermodynamics equation.
It could be, you know, Schrodinger's equation for quantum physics.
And so you could put any, you could have this describe almost anything, so long as you have the input and the output.
So long as you have the input and the output.
Or it could learn the input and output.
And so we took a step back and we said, hang on a second, this isn't just for computer vision.
Deep learning could solve any problem.
All the problems that are interesting.
So long as we have input and output.
Now, what has input and output?
Well, the world.
The world has input and output.
And so we could have a computer that could learn almost anything, machine learning, artificial intelligence.
And so we reasoned that maybe this is the fundamental breakthrough that we needed.
There were a couple of things that had to be solved.
For example, we had to believe that you could actually scale this up to giant systems.
It was running in a, they had two graphics cards, two GTX 580s, which, by the way, is exactly your SLI configuration.
Yeah.
Okay.
So that GTX 580 SLI was the revolutionary computer that put deep learning on the map.
We were lucky because we were inventing this technology, this computing approach.
We were lucky that they found it.
Turns out they were gamers and it was lucky they found it.
And it was lucky that we paid attention to that moment.
It was a little bit like, you know, that Star Trek, you know, first contact.
The Vulcans had to have seen the warped drive at that very moment.
If they didn't witness the warped drive, you know, they would have never come to Earth.
And everything would have never happened.
It's a little bit like if I hadn't paid attention to that moment, that flash, and that flash didn't last long, if I hadn't paid attention to that flash or our company didn't pay attention to it, who knows what would have happened.
But we saw that and we reasoned our way into this is a universal function approximator.
This is not just a computer vision approximator.
We could use this for all kinds of things if we could solve two problems.
The first problem is that we have to prove to ourselves it could scale.
The second problem we had to wait for, I guess, contribute to and wait for, is the world will never have enough data on input and output where we could supervise the AI to learn everything.
For example, if we have to supervise our children on everything they learn, the amount of information they could learn is limited.
We needed the AI.
We needed the computer to have a method of learning without supervision.
And that's where we had to wait a few more years.
But unsupervised AI learning is now here.
And so the AI could learn by itself.
And the reason why the AI could learn by itself is because we have many examples of right answers.
Like, for example, if I want to learn, if I want to teach an AI how to predict the next word, I could just grab it, grab up a whole bunch of text that we already have, mask out the last word, and make it try and try and try again until it predicts the next one.
Or I mask out random words inside the text, and I make it try and try and try until it predicts it.
You know, like Mary goes down to the bank.
Is it a riverbank or a money bank?
Well, if you're going to go down to the bank, it's probably a riverbank.
And it might not be obvious even from that.
It might need and caught a fish.
Okay, now you know it must be the riverbank.
And so you give these AIs a whole bunch of these examples and you mask out the words, it'll predict the next one.
And so unsupervised learning came along.
These two ideas, the fact that it's scalable and unsupervised learning came along, we were convinced that we ought to put everything into this and help create this industry because we're going to solve a whole bunch of interesting problems.
And that was in 2012.
By 2016, I had built this computer called the DGX1.
The one that you saw me give to Elon is called DGX Spark.
The DGX1 was $300,000.
It cost NVIDIA a few billion dollars to make the first one.
And instead of two chips SLI, we connected eight chips with a technology called NV-Link.
But it's basically SLI supercharged.
Okay?
And so we connected eight of these chips together instead of just two.
And all of them worked together, just like your Quake rig did, to solve this deep learning problem, to train this model.
And so we created this thing.
I announced it at GTC and at one of our annual events.
And I described this deep learning thing, computer vision thing, and this computer called DGX1.
The audience was like completely silent.
They had no idea what I was talking about.
And I was lucky because I had known Elon, and I helped him build the first computer for Model 3, the Model S.
And when he wanted to start working on autonomous vehicle, I helped him build the computer that went into the Model S A V system, his full self-driving system.
We were basically the FSD computer version one.
And so we're already working together.
And when I announced this thing, nobody in the world wanted it.
I had no purchase orders, not one.
Nobody wanted to buy it.
Nobody wanted to be part of it.
Except for Elon.
He goes, he was at the event, and we were doing a fireside chat about the future of self-driving cars.
I think it's like 2016.
Yeah, 20, maybe at that time it was 2015.
And he goes, you know what?
I have a company that could really use this.
I said, wow, my first customer.
And so I was pretty excited about it.
And he goes, yeah, we have this company.
It's a non-profit company.
And all the blood drained out of my face.
Yeah.
I just spent a few billion dollars building this thing.
It cost $300,000.
And, you know, the chances of a non-profit being able to pay for this thing is approximately zero.
And he goes, you know, this is an AI company, and it's a non-profit.
And we could really use one of these supercomputers.
And so I picked it up.
I built the first one for ourselves.
We're using it inside the company.
I boxed one up.
I drove it up to San Francisco and I delivered it to Elon in 2016.
A bunch of researchers were there.
Peter Beale was there.
Ilya was there.
There was a bunch of people there.
And I walked up to the second floor where they were all kind of in a room smaller than your place here.
I mean, if you wanted to make a story for a film, I mean, that would be the story that, like, What better scenario if it really does become a digital life form, how funny would it be that it is birthed out of the desire for computer graphics for video games?
When NVIDIA started in 1993, we were trying to create this new computing approach.
The question is, what's the killer app?
And the problem we wanted to, the company wanted to create a new type of computing computing architecture, a new type of computer that can solve problems that normal computers can't solve.
Well, the applications that existed in the industry in 1993 are applications that normal computers can solve.
Because if the normal computers can't solve them, why would the application exist?
And so we had a mission statement for a company that has no chance of success.
And so, in exchange for them developing their games for our computers in the PC, we would build a chip for their game console.
That was the partnership.
I build a chip for your game console, you port the Sega games to us.
And then they paid us, you know, at the time, quite a significant amount of money to build that game console.
And that was kind of the beginning of NVIDIA getting started.
And we thought we were on our way.
And so I started with a business plan, a mission statement that was impossible.
We lucked into the Sega partnership.
We started taking off, started building our game console.
And about a couple years into it, we discovered our first technology didn't work.
It would have been a flaw.
It was a flaw.
And all of the technology ideas that we had, the architecture concepts were sound, but the way we were doing computer graphics was exactly backwards.
You know, instead of, I won't bore you with the technology, but instead of inverse texture mapping, we were doing forward texture mapping.
Instead of triangles, we did curved surfaces.
So other people did it flat.
We did it round.
Other technology, the technology that ultimately won the technology we use today, has Z buffers.
It automatically sorted.
We had an architecture with no Z buffers.
The application had to sort it.
And so we chose a bunch of technology approaches that three major technology choices, all three choices were wrong.
Okay, so this is how incredibly smart we were.
And so in 1995, mid-95, we realized we were going down the wrong path.
Meanwhile, the Silicon Valley was packed with 3D graphics startups because it was the most exciting technology of that time.
And so 3DFX and Rendition and Silicon Graphics was coming in.
Intel was already in there.
And, you know, gosh, what added up eventually to a hundred different startups we had to compete against.
Everybody had chosen the right technology approach, and we chose the wrong one.
And so we were the first company to start.
We found ourselves essentially dead last with the wrong answer.
And so the company was in trouble.
And ultimately, we had to make several decisions.
The first decision is: well, if we change now, we will be the last company.
And even if we changed into the technology that we believe to be right, we'd still be dead.
And so that argument, you know, do we change and therefore be dead?
Don't change and make this technology work somehow?
Or go do something completely different.
That question stirred the company strategically and was a hard question.
I eventually advocated for we don't know what the right strategy is, but we know what the wrong technology is.
So let's stop doing it the wrong way and let's give ourselves a chance to go figure out what the strategy is.
The second thing, the second problem we had was our company was running out of money and I was in a contract with Sega and I owed them this game console.
And if that contract would have been canceled, we'd be dead.
We would have vaporized instantly.
And so I went to Japan and I explained to the CEO of Sega, Iri Majuri, really great man.
He was the former CEO of Honda USA.
Went back to Sega to run Sega.
I went back to Japan and run Sega.
And I explained to him that I was, I guess I was, what, 30, 33 years old.
You know, when I was 33 years old, I still had acne.
And I got this Chinese kid, I was super skinny.
And he was already kind of elder.
And I went to him and I said, listen, I've got some bad news for you.
And first, the technology that we promised you doesn't work.
And second, we shouldn't finish your contract because we'd waste all your money and you would have something that doesn't work.
And I recommend you'd find another partner to build your game console.
And so I'm terribly sorry that we've set you back in your product roadmap.
And third, even though I'm asking you to let me out of the contract, I still need the money.
Because if you didn't give me the money, we'd vaporize overnight.
And so I explained it to him humbly, honestly.
I gave him the background.
I explained to him why the technology doesn't work, why we thought it was going to work, why it doesn't work.
And I asked him to convert the last $5 million that they were going to complete the contract to give us that money as an investment instead.
And he said, but it's very likely your company will go out of business, even with my investment.
And it was completely true.
Back then, 1995, $5 million was a lot of money.
It's a lot of money today.
$5 million was a lot of money.
And here's a pile of competitors doing it right.
What are the chances that giving NVIDIA $5 million, that we would develop the right strategy, that he would get a return on that $5 million or even get it back?
Zero percent.
You do the math, it's zero percent.
If I were sitting there right there, I wouldn't have done it.
$5 million was a mountain of money to Sega at the time.
And so I told him that if you invested that $5 million in us, it is most likely to be lost.
But if you didn't invest that money, we'd be out of business and we would have no chance.
And I told him that I don't even know exactly what I said in the end, but I told him that I would understand if he decided not to, but it would make the world to me if he did.
He went off and thought about it for a couple days and came back and said, we'll do it.
And if he would have kept that five, the investment, I think it'd be worth probably about a trillion dollars today.
I know.
But the moment we went public, they sold it.
They go, wow, that's a miracle.
And so they sold it.
Yeah, they sold it at NVIDIA valuation about $300 million.
That's our IPO valuation, $300 million.
Wow.
And so, anyhow, I was incredibly grateful.
And then now we had to figure out what to do because we still were doing the wrong strategy, wrong technology.
So unfortunately, we had to lay off most of the company.
We shrunk the company all back.
All the people working on the game console, you know, we had to shrink it all back.
And then somebody told me that, but Jensen, we've never built it this way before.
We've never built it the right way before.
We've only known how to build it the wrong way.
And so nobody in the company knew how to build this supercomputing image generator 3D graphics thing that Silicon Graphics did.
And so I said, okay, how hard can it be?
You got all these 30 companies, 50 companies doing it.
How hard can it be?
And so, luckily, there was a textbook written by the company, Silicon Graphics.
And so I went down to the store.
I had 200 bucks in my pocket.
And I bought three textbooks, the only three they had, $60 a piece.
I bought the three textbooks.
I brought it back and I gave one to each one of the architects.
And I said, read that and let's go save the company.
And so they read this textbook, learned from the giant at the time, Silicon Graphics, about how to do 3D graphics.
But the thing that was amazing, and what makes NVIDIA special today, is that The people that are there are able to start from first principles, learn best-known art, but re-implement it in a way that's never been done before.
And so, when we reimagined the technology of 3D graphics, we reimagined it in a way that manifests today the modern 3D graphics.
We really invented modern 3D graphics, but we learned from previous known arts and we implement it fundamentally differently.
Well, you know, ultimately, ultimately, the simple answer is that the way silicon graphics works, the geometry engine is a bunch of software running on processors.
We took that and eliminated all the generality, the general purposeness of it, and we reduced it down into the most essential part of 3D graphics.
And we hard-coded it into the chip.
And so, instead of something general purpose, we hard-coded it very specifically into just the limited applications, limited functionality necessary for video games.
And that capability, that super, and because we reinvented a whole bunch of stuff, it supercharged the capability of that one little chip.
And our one little chip was generating images as fast as a $1 million image generator.
That was the big breakthrough.
We took a million-dollar thing and we put it into the graphics card that you now put into your gaming PC.
And that was our big invention.
And then, and of course, the question is: how do you compete against these 30 other companies doing what they were doing?
And there we did several things.
One, instead of building a 3D graphics chip for every 3D graphics application, we decided to build a 3D graphics chip for one application.
We bet the farm on video games.
The needs of video games are very different than needs for CAD, needs for flight simulators.
They're related but not the same.
And so we narrowly focused our problem statement so I could reject all of the other complexities, and we shrunk it down into this one little focus, and then we supercharged it for gamers.
And the second thing that we did was we created a whole ecosystem of working with game developers and getting their games ported and adapted to our silicon so that we could turn essentially what is a technology business into a platform business, into a game platform business.
So GeForce is really today, it's also the most advanced 3D graphics technology in the world.
But a long time ago, GeForce is really the game console inside your PC.
It runs Windows, it runs Excel, it runs PowerPoint, of course, those are easy things.
But its fundamental purpose was simply to turn your PC into a game console.
So we were the first technology company to build all of this incredible technology in service of one audience, gamers.
Now, of course, in 1993, the gaming industry didn't exist.
But by the time that John Carmack came along and the Doom phenomenon happened, and then Quake came out, as you know, that entire world, that entire community boom, took off.
It came from this there's a scene in the movie, The Color of Money, where Tom Cruise, who's this elite pool player, shows up at this pool hall and this local hustler says, What do you got in the case?
And he opens up this case.
He has a special pool queue.
He goes in here, and he opens it up, he goes, Doom.
$5 million, that pivot, with that conversation with that gentleman, if he did not agree to that, if he did not like you, what would the world look like today?
The performance, cost-performance ratio of 3D graphics for gaming was off the charts amazing.
And we're getting ready to ship it.
We're building it.
So, as you know, $5 million doesn't last long.
And so every single month, every single month, we were drawing down.
You have to build it, prototype it, you have to design it, prototype it, get the silicon back, which costs a lot of money.
Test it with software.
Because without the software testing the chip, you don't know the chip works.
And then you're going to find a bug, probably, because every time you test something, you find bugs, which means you have to tape it out again, which is more time, more money.
And so we did the math.
There was no chance somebody was going to survive it.
We didn't have that much time to tape out a chip, send it to a foundry, TSMC, get the silicon back, test it, send it back out again.
There was no shot, no hope.
And so the math, the spreadsheet, doesn't allow us to do that.
And so I heard about this company, and this company built this machine.
And this machine is an emulator.
You could take your design, all of the software that describes the chip, and you could put it into this machine.
And this machine will pretend it's our chip.
So I don't have to send it to the fab, wait until the fab sends it back.
I could have this machine pretend it's our chip, and I could put all of the software on top of this machine called an emulator and test all of the software on this pretend chip, and I could fix it all before I send it to the fab.
And if I could do that, when I send it to the fab, it should work.
Nobody knows, but it should work.
And so we came to the conclusion that let's take half of the money we had left in the bank.
At the time, it was about a million dollars.
Take half of that money and go buy this machine.
So instead of keeping the money to stay alive, I took half of the money to go buy this machine.
Well, I called this guy up.
The company's called Icos.
Call this company up and I said, hey, listen, I heard about this machine.
I like to buy one.
And they go, oh, that's terrific, but we're out of business.
I said, what?
You're out of business.
He goes, yeah, we had no customers.
And I said, wait, hang on a second.
So you never made the machine?
They could say, no, no, no, we made the machine.
We have one in inventory if you want it, but we're out of business.
And on this machine, we put NVIDIA's chip into it.
And we tested all of the software on top.
And at this point, we were on fumes.
But we convinced ourselves that chip is going to be great.
And so I had to call some other gentleman.
So I called TSMC.
And I told TSMC that listen, TSMC is the world's largest founder today.
At the time, they were just a few hundred million dollars large.
Tiny little company.
And I explained to them what we were doing.
And I explained to my, I told them I had a lot of customers.
I had one, you know, Diamond Multimedia, probably one of the companies you bought the graphics card from back in the old days.
And I said, you know, we have a lot of customers and the demand's really great.
And we're going to tape out a chip to you.
And I like to go directly to production because I know it works.
And they said, nobody has ever done that before.
Nobody has ever taped out a chip that worked the first time.
And nobody starts out production without looking at it.
But I knew that if I didn't start the production, I'd be out of business anyways.
And if I could start the production, I might have a chance.
And so TSMC decided to support me.
And this gentleman is named Morris Chang.
Morris Chang is the father of the foundry industry, the founder of TSMC, really great man.
He decided to support our company.
I explained to them everything.
He decided to support us, frankly, probably because they didn't have that many other customers anyhow, but they were grateful.
And I was immensely grateful.
And as we were starting the production, Morris flew to the United States and he didn't so many words ask me so, but he asked me a whole lot of questions that was trying to tease out, do I have any money?
But he didn't directly ask me that, you know.
And so the truth is that we didn't have all the money.
But we had a strong PO from the customer.
And if it didn't work, some wafers would have been lost.
I'm not exactly sure what would have happened, but we would have come short.
It would have been rough.
But they supported us with all of that risk involved.
We launched this chip.
Turns out to have been completely revolutionary.
Knocked the ball out of the park.
We became the fastest growing technology company in history to go from zero to one billion dollars.
I think that's important for a lot of people to hear because, you know, there's probably some young people out there that are in a similar position to where you were when you were starting out that just feel like, oh, those people that have made it, they're just smarter than me and they had more opportunities than me.
And it's just like it was handed to them or they're just in the right place at the right time.
I would imagine one of the more difficult aspects of your job currently, now that the company is massively successful, is anticipating where technology is headed and where the applications are going to be.
And NVIDIA is now, if you look at the large tech companies in the world today, most of them have a business in advertising or social media or content distribution.
And at the core of it is really fundamental computer science.
And so the company's business is not computers.
The company's business is not technology.
Technology drives the company.
NVIDIA is the only company in the world that's large whose only business is technology.
We only build technology.
We don't advertise.
The only way that we make money is to create amazing technology and sell it.
And so to be that, to be NVIDIA today, the number one thing is you're surrounded by the finest computer scientists in the world.
And that's my gift.
My gift is that we've created a company's culture, a condition by which the world's greatest computer scientists want to be part of it.
Because they get to do their life's work and create the next thing.
Because that's what they want to do.
Because maybe they don't want to be in service of another business.
They want to be in service of the technology itself.
And we're the largest form of its kind in the history of the world.
And so one, you know, we have got a great condition.
We have a great culture.
We have great people.
And now the question is how do you systematically be able to see the future, stay alert of it, and reduce the likelihood of missing something or being wrong.
And so there's a lot of different ways you could do that.
For example, we have great partnerships.
We have fundamental research.
We have a great research lab, one of the largest industrial research labs in the world today.
And we partner with a whole bunch of universities and other scientists.
We do a lot of open collaboration.
And so I'm constantly working with researchers outside the company.
We have the benefit of having amazing customers.
And so I have the benefit of working with Elon and others in the industry.
And we have the benefit of being the only pure play technology company that can serve consumer internet, industrial manufacturing, scientific computing, healthcare, financial services, all the industries that we're in, they're all signals to me.
And so they all have mathematicians and scientists.
And so because I have the benefit now of a radar system that is the most broad of any company in the world, working across every single industry, from agriculture to energy to video games.
And so the ability for us to have this vantage point, one, doing fundamental research ourselves, and then two, working with all the great researchers, working with all the great industries, the feedback system is incredible.
And then finally, you just have to have a culture of staying super alert.
There's no easy way of being alert except for paying attention.
I haven't found a single way of being able to stay alert without paying attention.
And so, you know, I probably read several thousand emails a day.
It really, I mean, it has to be kind of surreal to be in the position that you're in now when you look back at how many times that it could have fallen apart and humble beginnings.
My parents sent my older brother and I here first.
We're in Thailand.
I was born in Taiwan, but my dad had a job in Thailand.
He was a chemical and instrumentation engineer, incredible engineer.
And his job was to go start an oil refinery.
And so we moved to Thailand, lived in Bangkok.
And in 19, I guess, 1973, 1974 timeframe, you know how Thailand, every so often, they would just have a coup.
You know, the military would have an uprising.
And all of a sudden, one day, there were tanks and soldiers in the streets.
And my parents thought, you know, it probably isn't safe for the kids to be here.
And so they contacted my uncle.
My uncle lives in Tacoma, Washington.
And we had never met him.
And my parents sent us to him.
How old are you?
I was about to turn nine, and my older brother almost turned 11.
And so the two of us came to the United States.
And we stayed with our uncle for a little bit while he looked for a school for us.
And my parents didn't have very much money.
And they'd never been to the United States.
My father was, I'll tell you that story in a second.
And so my uncle found a school that would accept foreign students and affordable enough for my parents.
And that school turned out to have been in Oneida, Kentucky, Clark County, Kentucky, the epicenter of the opioid crisis today.
Coal country.
Clark County, Kentucky was the poorest county in America when I showed up.
It is the poorest county in America today.
And so we went to the school.
It's a great school.
Oneida Baptist Institute in a town of a few hundred.
I think it was 600 at the time that we showed up.
No traffic light.
And I think it has 600 today.
It's kind of an amazing feat, actually.
The ability to hold your population for when it's 600 people is quite a magical thing, however, they did it.
And so the school had a mission of being an open school for any children who'd like to come.
And what that basically means is that if you're a troubled student, if you have a troubled family, if you're, you know, whatever your background, you're welcome to come to Oneida Baptist Institute, including kids from international who would like to stay there.
I know his name, but I don't know where he is now.
But anyways, that night we got – and the second thing I noticed when you walk into your dorm room is there are no drawers and no closet doors.
Just like a prison.
And there are no locks so that people could check up on you.
And so I go into my room, and he's 17, and get ready for bed.
And he had all this tape all over his body.
And it turned out he was in a knife fight, and he's been stabbed all over his body, and these were just fresh wounds.
And the other kids were hurt much worse.
And so he was my roommate, the toughest kid in school.
And I was the youngest kid in school.
It was a junior high, but they took me anyways because if I walked about a mile across the Kentucky River, the swing bridge, the other side is a middle school that I could go to.
And then I can go to that school and I come back and I stay in the dorm.
And so basically, Oneida Baptist Institute was my dorm when I went to this other school.
My older brother went to the junior high.
And so we were there for a couple of years.
Every kid had chores.
My older brother's chore was to work in the tobacco farm.
You know, so tobacco, they raised tobacco so they could raise some extra money for the school, kind of like a penitentiary.
Wow.
And my job was just to clean the dorm.
And so I was nine years old.
I was cleaning toilets for a dorm of 100 boys.
I cleaned more bathrooms than anybody.
And I just wish that everybody was a little bit more careful.
And what breaks my heart, probably the only thing that really breaks my heart about that experience was so we didn't have enough money to make international phone calls every week.
And so my parents gave us this tape deck, this IWA tape deck, and a tape.
And so every month we would sit in front of that tape deck and my older brother, Jeff, and I the two of us would just tell them what we did the whole month.
Could you imagine if for two years that tape still existed of these two kids just describing their first experience with the United States?
Like I remember telling my parents that I joined the swim team and my roommate was really buff and so every day we spent a lot of time in the in the gym and so every night 100 push-ups, 100 sit-ups every day in the gym.
So I was nine years old.
I was getting I was pretty buff.
And I'm pretty fit.
And so I joined the soccer team.
I joined the swim team because if you join the team, they take you to meets, and then afterwards, you get to go to a nice restaurant.
And that nice restaurant was McDonald's.
Wow.
And I recorded this thing.
I said, Mom and Dad, we went to the most amazing restaurant today.
And when they came to the United States, they had almost no money.
Probably one of the most impactful memories I have is they came and we were staying in an apartment complex.
And they had just rent back in the, I guess people still do rent a bunch of furniture.
And we were messing around.
And we bumped into the coffee table and crushed it.
It was made out of particle wood.
We crushed it.
And I just still remember the look on my mom's face, you know, because they didn't have any money and she didn't know how she was going to pay it back.
But anyhow, that kind of tells you how hard it was for them to come here.
But they left everything behind.
And all they had was their suitcase and the money they had in their pocket when they came to the United States.
So that was the beginning of it, is just seeing other people do it.
And then saying, all right, let's just try it.
And then so the beginning days, we just did it on a laptop, had a laptop with a webcam and just messed around, had a bunch of comedians come in, we would just talk and joke around.
Then I did it like once a week.
And then I started doing it twice a week.
And then all of a sudden, I was doing it for a year.
And then I was doing it for two years.
Then it was like, oh, it's starting to get a lot of viewers and a lot of listeners.
You know, it's an amazing gift to be able to have so many conversations with so many interesting people because it changes the way you see the world because you see the world through so many different people's eyes.
And you have so many different people of different perspectives and different opinions and different philosophies and different life stories.
And, you know, it's an incredibly enriching and educating experience having so many conversations with so many amazing people.
And that's all I started doing.
And that's all I do now.
Even now, when I book the show, I do it on my phone.
And I basically go through this giant list of emails of all the people that want to be on the show or that request to be on the show.
And then I factor in another list that I have of people that I would like to get on the show that I'm interested in.