Jensen Huang on NVIDIA's AI Revolution and Future

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The following is a conversation with Jensen Quallan, CEO of Nvidia, one of the most important and influential companies in the history of human civilization.

Nvidia is the engine powering the AI revolution, and a lot of its success can be directly attributed to Jensen's share force of will, and his many brilliant bets and decisions as a leader, engineer, and innovator.

And now a quick few second mention of his sponsor, check them out in the description or at lexfreedman.com slash sponsors.

It is in fact the best way to support this podcast.

We got Shopify for selling stuff online, element for electrolytes, thin for customer service AI agents, Quall for a phone system like calls, texts, contacts for your business, and complexity for curiosity driven knowledge exploration.

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All right, let's go.

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And now, dear friends, here's Justin Huang.

UF propelled in video into a new era in AI.

Moving beyond his focus on chip scale design to now rack scale design.

And I think it's fair to say that winning for Nvidia for a long time used to be about building the best GPU possible.

And you still do, but now you've expanded that to extreme co-design of GPU, CPU, memory, networking, storage, power, cooling, software.

The rack itself, the pod, that you've announced, and even the data center.

So let's talk about extreme co-design.

What is the hardest part of co-designing system with that many complex components and design Yeah, thanks for that question.

So first of all, the reason why extreme co-design is necessary is because the problem no longer fits inside one computer to be accelerated by one GPU.

The problem that you're trying to solve is you would like to go faster than the number of computers that you add.

So you added, you know, 10,000 computers, but you would like it to go a million times faster.

Then all of a sudden, you have to take the algorithm, you have to break up the algorithm, you have to refactor it, you have to shard the pipeline, you have to shard the data, you have to shard the model.

Now all of a sudden, when you distribute the problem this way, not just scaling up the problem, but you're distributing the problem, then everything gets in the way.

This is the Amdahl's law problem where the amount of speed up you have for something depends on how much of the total workload it is.

And so if computation represents 50% of the problem and I speed up computation infinitely, like a million times, you know, I only speed up the total workload by effect of two.

Now all of a sudden, not only do you have to distribute the computation, you have to, you know, shard the pipeline somehow, you also have to solve the networking problem.

Because you've got all of these computers are all connected together.

And so, distributed computing at the scale that we do.

The CPU's a problem, the GPU's a problem, the networking's a problem, the switching is problem.

And distributing the workload across all these computers are a problem.

It's just a massively complex computer science problem.

So we just got to bring every technology to bear.

Otherwise, we scale up linearly, or we scale up based on the capabilities of Moore's law, which has largely slowed, because Bernard scaling has slowed.

I'm sure there's trade-offs there.

Plus, you have a complete disparate disciplines here.

I'm sure you have specialists in each one of these, high bandwidth memory, the networking, the envy link, the next, the optics and the copper that you're doing, the power delivery, the cooling, all of that.

I mean, there's like world experts in each of those, how do you get them in a room together to figure out my staff is so large?

What's the problem?

You take it to the process of the specialists and the journalists, how do you put together the rack when you know the set of things you have to shove into a rack together?

What does that process look like?

Of the setting it all together?

There's the first question, which is, what is extreme code design?

You were optimizing across the entire stack of software from architectures to chips to systems, the system software to the algorithms to the applications.

That's one layer.

The second thing that you and I just talked about is, goes beyond CPUs and GPUs and networking chips and scale-ups, switches and scale-outs, switches.

And then of course you got to include power and cooling and all of that because you know, all these computers are extremely, extremely power, power hungry.

They do a lot of work and they're very energy efficient, but these in aggregate still consume a lot of power.

And so that's one, the first question is of what is it?

The second question is, why is it?

And we just spoke about the reason, you know, you want to distribute the workload so that you can exceed the benefit of just increasing the number of computers.

And then the third question is, how is it?

How do you do it?

And that's the miracle of this company.

When you're designing your computer, you have to have the operating system of computers.

When you're designing a company, you should first think about, what is it that you want the company to produce?

I see a lot of companies organization charts and they all look the same.

Hamburger organization charts, soft organization charts and car company organization charts, they all look the same.

And it doesn't make any sense to me.

You know, the goal of a company is to be the machinery, the mechanism, the system that produces the output.

And that output is the product that we like to create.

It is also designed the architecture of the company should reflect the environment by which it exists.

It almost directly says what you should do with the organization might direct staff as 60 people.

You know, I don't have one on one with them because it's impossible.

You can't have 60 people on your staff.

If you're, you know, going to get worked on.

And you still have 60 reports.

You still have more.

Yeah.

Yeah.

And most stars, at least have a foot in engineering.

Almost all of them.

There's experts in memory.

There's experts in CPUs.

There's experts in optical, all of them.

Yeah, GPUs and architecture algorithms designed.

So you constantly have an eye in the entire stack.

And you're having, you're like intense discussions about the design of the entire stack.

And no conversation is ever one person.

That's why I don't do one of ones.

We present a problem.

And all of us attack it.

You know, because we're doing extreme code design.

And literally the company is doing extreme code design all the time.

So even if you're talking about a particular component, like cooling, networking, everybody's listening in.

Yeah.

And it can contribute.

Well, this doesn't work for the power distribution.

This doesn't work for the memory.

This doesn't work for this.

Exactly.

And whoever wants to tune out to now, you know, I'm saying.

Yeah.

And the reason for that is because because the people who are on the staff, they know when to pay attention.

They're supposed, you know, it's something they could have contributed to.

They didn't contribute to.

I'm going to call them out.

You know, and so hey, come on.

It's good in here.

So as you mentioned in videos, this company has adapting to the environment.

So which point can you say, did the environment change, began adapting sort of secretly in the early days from GPU, for gaming, maybe the early deep learning revolution, too.

We're not going to start thinking of it as an AI factory.

What does Nvidia do?

It produces AI.

Let's build the factory.

Oh, yeah.

I could reason through it just systematically.

We started out as an accelerator company.

But the problem with accelerators is that the application domain is too narrow.

It has the benefit of being incredibly optimized for the job.

You know, any specialist has that benefit.

The problem with intense specialization is that, of course, your market reach is narrower.

But that's, that's even fine.

The problem is the market size also dictates your R&D capacity.

And your R&D capacity ultimately dictates the influence and impact that you can possibly have in computing.

And so when we first started out as an accelerator, a very specific accelerator, we always knew that that was going to be our first step.

We had to find a way to become accelerated computing.

But the problem is when you become a computing company, it's too general purpose and it takes away from your specialization.

I connected two words there are actually have fundamental tension.

The better computing company we become, the worse we became as a specialist.

The more of a specialist, the less capacity we have to do overall computing.

And so that, and I connected those two words together on purpose, that the company has to find that really narrow path, step by step by step, to expand our aperture of computing, but not give up on the most important specialization that we had.

Okay, so the first step that we took beyond acceleration was we invented the programmable pixel shader.

So that was the first step towards programmability.

You know, I was our first journey towards moving into the world of computing.

The second thing that we did was we created, we put FP32 into our shaders.

That FP32 step, I triply compatible FP32, was a huge step in the direction of computing.

It was the reason why all of the people who were working on on stream processors and other types of data flow processors discovered us.

And they said, hey, all of a sudden, you know, we might be able to use these GPUs as incredibly computationally intensive.

And it's now, you know, compliant with it, triply.

I can take my software that I was writing, you know, previously on CPUs.

And I can, you know, see about, you know, using the GPU for that.

And which led us to create, put C on top of FP32 was called, we call CG.

That CG path took us to eventually Cuda, Cuda step by step by step.

We, well, putting Cuda on G4s, that that was a strategic decision that was very, very hard to do because it cost the company enormous amounts of our profits.

And we couldn't afford it at the time.

But we did it anyways because we wanted to be a computing company.

A computing company has a computing architecture, a computing architecture, has to be compatible across all of the chips that we built.

Can you, can you take me to that decision?

So putting Cuda on G4s could not afford to do, can you explain that decision?

Why why boldly choose to do that anyway?

Can you explain that decision?

That was, that was the first, I would, I would say that that was the first, the first strategic decision that that is it's close to an existential threat.

For people who don't know, it turned out to be spoiler alert, one of the most incredibly brilliant decisions ever made by a company.

So Cuda turned out to be an incredible foundation for computation in this AI infrastructure world.

So, so you're just setting the context.

It turned out to be a good decision.

Yeah, it turned out to a big good decision.

I think the, so here's the way it went.

So we invented this thing called Cuda and it expanded the aperture of applications that that we can accelerate with our accelerator.

The question is how do we, how do we attract developers to Cuda?

Because a computing platform is all about developers.

And developers don't come to a computing platform just because, you know, it could perform something interesting.

They come to a computing platform because the install base is large.

Because a developer like anybody else wants to develop software that reaches a lot of people.

So the install base is in fact the single most important part of an architecture.

The architecture could attract enormous amounts of criticism.

For example, no architecture is ever attracted more criticism than the X86.

And, you know, as a less than less than elegant architecture.

But yet, it is the defining architecture of today.

It gives you an example.

Then, in fact, so many risk architectures, which were beautifully architected, incredibly well designed by some of the brightest computer scientists in the world, largely failed.

And so I've gave you two examples where one is, you know, one's elegant, the other one's barely aesthetic.

And so yet X86 survived.

And the install base is everything.

Install base defines an architecture.

Not everything else is secondary, okay?

And so there were other architectures at the time.

CUDA came out, open CL was here.

There were, you know, there's several other computing architectures.

But the thing that the decision that we made that was good was we said, hey, look, ultimately it's about install base.

And what is the best way we could get a new computing architecture into the world?

By that timeframe, G4s had become successful.

We were already selling millions of millions of G4s GPUs a year.

And we said, you know, we had to put CUDA on G4s and put it into every single PC where their customers use it or not.

And use it as a starting point of cultivating our install base.

Meanwhile, we'll go and attract developers and went to universities and wrote books and taught classes and put CUDA everywhere.

And eventually people discover, and at the time the PC was the primary computing vehicle, there was no cloud.

And we could put a super computer in the hands of every researcher in school, every scientist, you know, every engineering school, ever, or every student school.

And eventually something amazing will happen.

While the problem was CUDA increased our cost of that GPU, which is a consumer product.

So tremendously, it completely consumed all of the company's gross profit dollars.

And so at the time, the company was probably, you know, worth, I don't know at the time.

Was it like $8 billion or something, a $67 billion dollars or something like that?

After we launched CUDA, I recognized that it was going to add so much cost, but it was something we believed in.

You know, our market cap went down until like one and a half billion dollars.

And so we were down there for a while.

And we clawed our way back slowly.

But we carried CUDA on G-Force.

I always say that Nvidia is the house that G-Force built.

Because it was G-Force that took CUDA out to everybody.

Researchers, scientists, they discovered CUDA on G-Force.

Because they were all, you know, many of them were gamers, many of them built their own PCs anyways.

And a universe thee lab, many of them built clusters themselves, you know, using PC components.

And so that, you know, that's kind of how we got going.

And then that became the platform and the foundation for the deep learning revolution.

That was also another great great observation.

Yeah.

That existential moment.

Do you remember what were those meetings like?

What were those discussions like deciding as a company, risking everything?

Well, I had, I had to make it clear to the board what we were trying to do.

And the management team knew our gross margins were to get crushed.

So you could imagine a world where G-Force would carry the burden of CUDA and none of the gamers would appreciate it and none of the gamers would pay for it.

You know, they only pay certain price.

And it doesn't matter what your cost is.

And so you know, we increased our cost by 50 percent.

And that could consume, and we were a 35 percent gross margin company.

And so it was a, it was quite a difficult decision to make.

But you could imagine that someday, just go into workstations and it would go into super computers and, and in those segments, maybe we can capture more margin.

So you could, you could reason your way into being able to afford this, but it still took, it took a decade.

But that's more, like, conversation with the board convincing them, but you psychologically has, and various continue to make bold bets that predict the future.

And in part, especially now, define the future.

So I'm almost looking for wisdom about how you're able to make those decisions to make leaps like that as a company.

Well, first of all, I'm informed by by a lot of curiosity.

At some point, there's a reasoning system that that convinces me so clearly this outcome will happen, that this will happen.

And so I believe, I believe it in my mind, and when I believe in in my mind, you know, you know, how it is, you manifest a future, and that future is so convincing, there's no way it won't happen.

There's a lot of suffering in between, but you've got to believe what you believe.

So you envision the future, you know, and you essentially from a sort of engineering perspective, yeah, and, and you, you reason about how to get there, you reason about why it must exist.

And, and, and, and, and, you know, I reason, we all reason here, the management team were reason about all the people that, like, we spent a lot of time reasoning about it.

The thing, the thing that the next part of it is probably a skill thing, which is, you know, oftentimes in leadership, the leadership stays quiet or they learn about something and then they do some manifesto, and it's a brand new year and somehow at the end of the year, next year, we're going to have a brand new plan, big usually off this way, big huge organization changed this way, new mission statement, brand new logos, you know, that kind of stuff.

We just never, I never do things that way.

When I learned about something and it started to influence how I think I'll make it very clear to everybody near me that, you know, this, this is interesting.

This is going to make a difference.

This is going to impact that.

And I reason about things step by step by step, oftentimes I've already made up my mind, but I'll take every possible opportunity, external information, new insights, new discoveries, new engineering, you know, revelations, new milestones develop.

I'll take those opportunities and I'll use it to shape everybody else's belief system.

And I'm doing that literally every single day.

I'm doing that with my board.

I'm doing that with my management team.

I'm doing that with my employees.

I'm trying to shape their belief system.

Such that when I come, the day I say, hey, let's buy melanox.

It's completely obvious to everybody that we absolutely should.

On the day that, on the day that I said, hey, guys, let's go all in on deep learning.

And let me tell you why.

I've already been laying down the bricks to different organizations inside the company.

Every organization and everybody, many of the people might have heard everything.

Most of the company heard here, of course, pieces of it.

And on the day that I announced it, everybody's kind of bought it into many pieces of it.

And in a lot of ways, I like to announce these things.

And I imagine that the employees are kind of saying, you know, Jensen, what took you so long.

And in fact, I've been shaping their belief system for some time.

And therefore, leadership, sometimes it looks like you're leading from behind.

But you've been shaping their, you know, to the point where on the day that I declared it, 100% buying it.

But that's what you want.

You want to bring everybody along.

You know, otherwise we announced something about deep learning.

And everybody goes, what are you talking about?

You know, you now said something about, let's go all in on this thing.

And your, your management team, your board, your employees, your customers, they're kind of like, where's this coming from?

You know, this isn't saying.

And so, so GTC, in fact, if you go back in time, you look at, look at the keynotes.

I'm also shaping the belief system of my partners and the industry and, and I'm using that to shape, you know, the belief system of my own employees.

And, and so by the time that I announced something, like for example, we just now, we just announced rock.

We've been late.

I've been talking about the stepping stones for two and a half years.

You guys just go back and you, oh my gosh, they've been talking about it for two and a half years.

And so I've been laying the foundation step by step by step.

So when the time comes, you announce it, everything, you know, what took you so long?

But it's not just inside the company, you're shaping the landscape, the broader global landscape of innovation, like putting those ideas out there, you really are manifesting reality.

We don't build computers.

We actually don't build clouds.

We don't, as it turns out, we're computing platform company.

And so, nobody can buy anything from us.

That's the weird thing.

You know, we vertically, design, vertically integrate to design and optimize.

But then we open up the entire platform at every single layer to be integrated into other companies, products and services and clouds and super computers and OEM computers.

And so the amazing thing is, I can't do what I do without having convinced them first.

And so most of GTC is about manifesting a future that by the time that we, my product is ready, they're going, what took you so long?

Yeah, so one of the things you've been a believer for a long time is scaling laws, broadly defined.

So are you still a believer in the scaling laws?

Yeah, we have more scaling laws now.

So I think, yeah, you've outlined four of them with pre-training post-training test time and agente scaling.

What do you think when you think about the future?

Deep future and the near term future.

What are the blockers that you're most concerned about that keep you up at night?

They have to overcome in order to keep scaling.

Well, we can go back and reflect on what people thought were blockers.

So in the beginning, we were the first pre-training scaling law.

People thought, well, rightfully so, that the amount of data that we have, high quality data that we have, will limit the intelligence that we achieve and that scaling law wasn't important, very important, scale law.

The larger the model, the correspondingly more data results in a better result in a smarter AI.

And so that was pre-training.

And Ilias, South Skipper, Ilias said, we're out of data or something that pre-training is over or something like that.

The industry panicked, you know, that this is the end of AI.

And of course, of course, that's obviously not true.

We're going to keep on scaling the amount of data that we have to to train with.

A lot of that data is probably going to be synthetic.

And that also confused people, you know, and what people don't realize is they've kind of forgotten that most of the data that we are training that we teach each other with and form each other with is synthetic.

You know, it's synthetic because it didn't come out of nature.

You created it.

I'm consuming it.

I modify it, augment it.

I regenerate it.

Somebody else consumes it.

And so, so we've now reached a level where AI is able to take ground truth, augment it, enhance it, synthetically generate an enormous amount of data.

And that part of post-training continues to scale.

And so the amount of data that we could use that is human-generated will be smaller, smaller, smaller, the amount of data that we use to train model is going to continue to scale.

To the point where we're no longer limited, training is no longer limited by data is now limited by compute.

And the reason for that is most of the data synthetic.

Then the next phase is a test time.

And I, I still remember people telling me that inference, oh yeah, that's easy.

Pre-training, that's hard.

These are giant systems that people are talking about, inference must be easy.

And so, inference chips are going to be a little tiny chips.

And, you know, they're not, they're not like in videos chips.

Oh, those are going to be complicated and expensive.

And, you know, we could make, and this is, and the future inference is going to be the biggest market.

And it's going to be easy.

And we're going to commoditize and, you know, everybody can build their own chips.

And, and that was always illogical to me because inference is thinking.

And I think thinking is hard.

Thinking is way harder than reading.

You know, pre-training is just memorization and generalization, you know, and looking for patterns and relationships here.

You're reading and reading versus thinking, reasoning, solving problems, taking un, un, unexplored experiences, new experiences, and breaking it down into decomposing it into, you know, solvable pieces that we then go off either through first principle, reasoning, or, you know, through previous examples, prior experiences, you know, or just exploration and, and, and search and, you know, trying different things.

And that whole process of, of, of test time scaling, the inference is really about thinking.

And, and it's about reasoning, it's about planning, it's about search, it's about, and so, how could that possibly be compute light?

And we were absolutely right about that, you know, so, so test time scaling is intensely compute intensive.

Then the question is, okay, now we're at inference and we're at test time scaling, what's beyond that?

Well, obviously, we have now created, you know, one agentic person.

And that one agentic person has a large language model that we've now, we've now, you know, developed.

But during test time that agentic system goes off and does research and bangs on databases and it goes and, you know, uses tools and one of the most important things it does is spins off and spawns off a whole bunch of sub agents, which means we're now creating large teams.

It's so much easier to scale and video by hiring more employees than it is to scale myself.

And so, the next scaling laws, the agentic scaling law, it's kind of like multiplying AI.

Multiplying AI, we could spin off agents as fast as you want to spin off agents.

And so, you know, you know, you're not have four scaling laws.

And as we use the agentic systems, they're going to create a lot more data, they're going to create a lot of experiences.

Some of it, we're going to say, wow, this is really good.

We ought to memorize this.

That data set then comes all the way back to pre-training.

We memorize and generalize it.

We then refine it and fine tune it back into post-training.

Then we enhance it even more with test time, you know, in the agent's agent's agentic systems, you know, put it on to the industry.

And so, this loop, the cycle is going to go on and on and on.

It kind of comes down to basically intelligence is going to scale by one thing and it's compute.

But there's a tricky thing there.

They have to anticipate and predict, which is some of these components, it requires different kind of hardware to really do it optimally.

So you have to anticipate where the AI innovation is going to lead, for example, make sure that it's personal sparsity.

Perfect.

With hardware, you can't just pivot on a weak notice.

You have to anticipate what that's going to look like.

That's so scary and difficult to do, right?

For example, these AI model architectures are being invented about once every six months, right?

And system architectures and hard work architectures kind of every three years.

And so you need to anticipate what likely is going to happen, you know, two-three years from now.

And there's a couple of ways that you could do that.

First of all, we could do research internally ourselves.

And that's one of the reasons why we have basic research.

We have applied research.

We create our own models.

And so we have hands on life experience right here.

This is part of the code design that I'm talking about.

We're also the AI company in the world that works with literally every AI company in the world.

And to the extent that we can, we try to get a sense of what are the challenges that people are experiencing.

So you're listening to the whispers across the industry, the ad labs.

That's right.

You've got to listen and learn from everybody and have and then the last part is to have an architecture that's that's flexible that can adapt and move with the wind and one of the benefits of of Kuda is that it's, you know, on the one hand, an incredible accelerator on the other hand, it's really flexible.

And so that balance, incredible balance between specialization, otherwise we can't accelerate it to CPU versus generalization so that we could adapt with changing algorithms.

That's really, really important.

That's the reason why Kuda has been so resilient on the one hand.

And yet we continue to enhance it where at Kuda 13.2.

And so we're evolving the architecture so fast that we can stay with, you know, with the modern algorithms.

For example, when mixture of experts came out, that's the reason why we had MV link 72 instead of MV link 8.

We could now take an entire 4 trillion, 10 trillion per hour model and put it in one computing domain as if it's running on one GPU.

People probably didn't notice.

I said it, but if you look at the architecture of the Grace Blackwall racks, it was completely focused on doing one thing, processing the LLM.

All of a sudden, one year later, you're looking at a very rubin' rack.

It has storage accelerators.

It has this incredible new CPU called Vera.

It has Vera Rubin and MV link 72 to run the LLMs.

It also has its new additional rack called Grok.

And so this entire rack system is completely different than the previous one.

And it's got all these new components in it.

And the reason for that is because the last one was designed to run MOE large language models, inference.

And this one is to run agents and agents bang on tools.

And obviously the design of the system had to have been done before clawed code code X, open claw to you are anticipating the future, essentially.

And that comes from what?

From the whispers, from the understanding what all the things they are.

No, it's easier than that.

You just reason about it.

First of all, it's just reason.

No matter what happens at some point in order for that large language model to be a digital worker, let's just use that metaphor.

Let's say that we want the LLM to be a digital worker.

What does it have to do?

It has to access ground truth.

That's our file system.

It has to be able to do research.

It doesn't know everything.

And I don't want to wait until this AI becomes universally smart about everything, past, present, and future before I make it useful.

And so therefore, I'm not as well let it go do research.

It's obviously, if it wants to help me, it's got to use my tools.

A lot of people would say, AI is going to completely destroy software.

We don't need software anymore.

We don't need tools anymore.

That's ridiculous.

Let's use the, let's use the thought experiment.

And you could just sit there and you're very glass of whiskey.

And think about all these things and it would come completely obvious.

Like if I were to create the most amazing role, the most amazing agent that we can imagine in the next 10 years.

Let's say be a human or robot.

If that human or robot were to be created, is it more likely that the human or robot comes into my house and uses the tools that I have to do to work that it needs to do?

Or does this hand turns into a 10 pound hammer in one instance?

Turns into a scalpel in another instance.

And in order to boil water, it beams, you know, microwaves out of its fingers.

Or is it more likely just to use a microwave?

And the first time it goes up to the microwave.

It probably doesn't know how to use it.

But that's okay.

It's connected to the internet.

It reads the manual of this microwave.

It reads it instantly becomes an expert.

And so it uses it.

And so I think the, I just described in fact, almost all of the properties of open claw.

You know, that it's going to use tools that it's going to access files.

It's going to be able to do research.

It has IO subsystem.

And when you're done reasoning about it through it in that way, then you say, oh my gosh, the impact to the future computing is deeply profound.

And the reason for that is, I think we just reinvented the computer.

And then now you say, okay, when did we reason about that?

When did we reason about open claw?

If you take the open claw schematic that I used at GTC, you'll find it two years ago.

Literally two years ago at GTC, I was talking about agentic systems that exactly reflect open claw today.

And of course, the confluence of many things had to happen.

First of all, we needed cloud and GPT and, you know, all of these models to reach a level of capability.

So their innovation and their breakthroughs and their continued advances was really important.

And then of course, somebody had to create an open source, you know, project that that was sufficiently robust.

You know, it's sufficiently complete.

And that we can all, we can all put to work.

And I think open claw did for, did for agentic systems, what chat GPT did for generative systems.

And I just think it's a very big deal.

Yeah, it's a really special moment.

I'm not exactly sure why captured so much of the world's attention, but it did more than cloud code and codex and so on.

Because consumers could reach it.

Sure.

Yeah, but there's also so much of this is vibes and Peter had a podcast with them, a wonderful human being.

So part of it is also the humans that represent the thing.

No part of it is memes.

Because we're all trying to figure it out.

There's really serious and complicated security concerns about when you have such poverty technology, how do you handle over your data so they can do useful stuff.

But then there's scary things associated with that.

And we're as a civilization as individual people and as a civilization figuring out how to find that right balance.

Yeah, we jumped on a right away and we sent a bunch of security experts his way.

And we did this thing called open shell.

It's already been integrated into open claw and it put forward an email claw.

Yeah, exactly.

They install super easy.

It makes sure that it's secure.

We give you two out of three rights.

Agentic systems can access sensitive information.

It can execute code and it can communicate externally.

We could keep things safe if we gave you two out of those three capabilities at any time, but not all three.

And out of those two out of three capabilities, we also give you access control based on based on whatever rights that you're given by enterprise.

And then we connected to a policy engine that all these enterprises already have.

And so we're going to try to do our best to help open claw become a better claw.

So you eloquently blame how we have a long history of blockers that we thought were going to be blocked as we overcame them.

But not looking into the future.

What do you think why be the blockers?

Now that it's clear that agents will be everywhere.

So it's obviously we're going to need compute.

So what is going to be the blocker for that scaling?

Power is a concern, but it's not the only concern.

But that's the reason why we're pushing so hard on extreme code design.

So that we can improve the tokens per second, per watt, orders of magnitude, every single year.

And so in the last 10 years, Moore's Law would have progressed computing about a hundred times in the last 10 years.

We progressed and scaled up computing by a million times in the last 10 years.

And so we're going to keep on doing that through extreme code design.

So energy efficiency, per watt, completely affects the revenues of a company.

It affects the revenues of a factory.

And we're just going to push that to a limit so that we can keep on driving token costs down.

As fast as we can.

You know, our computer price is going up.

But our token generation effectiveness is going up so much faster that token cost is coming down.

It's just, it's coming down an order magnitude every year.

So power, that's an interesting one.

So the way to try to get around the power blocker is to try to, with the tokens per second per watt, try to make it more and more efficient.

Of course, there's the question of how to get more power.

We should also get more power.

That's a really complicated one.

You've talked about small module, nuclear power plants, there's all kinds of ideas for energy.

How much does it keep you up at night?

The bottlenecks in the supply chain of AI, like ASML with the U.V.

lithography machines to SMC with advanced packaging, like co-oss and SK headaches with the high bandwidth memory.

All the time, and we're working on all the time.

No company in history has ever grown at a scale that we're growing while accelerating that growth.

It's incredible.

Yeah.

And it's hard for people to even understand this.

In the overall world of AI computing, we're increasing share.

And so supply chain, upstream and downstream are really important to us.

I spent a lot of time informing all the CEOs that I work with.

What are the dynamics that's going to cause the growth to continue or even accelerate?

It's part of the reasons why to the entire right-hand side of me were CEOs of practically the entire IT industry, upstream, and practically the entire infrastructure industry downstream.

And there were several hundred CEOs.

And I don't think there's ever been keynotes where several hundred CEOs show up.

And part of it is, I'm telling them about our business condition.

Now, I'm telling them about the growth drivers in the very near future and what's happening.

And I'm also describing where we're going to go next so that they could use all of this information and all of the dynamics that are here to inform how they want to invest.

And so I informed them that way like I am for my own employees.

And then of course, then I make trips out to them and make sure that, hey, listen, I want you to know, this quarter, this coming year, this next year, these things are going to happen.

And if you look at the CEOs of the DRAM industry, the number one DRAM in the world was DDR memory for CPUs and data centers.

About three years ago, I was able to convince several of the CEOs that even though at the time, HBM memory was used quite scarcely, you know, and barely by super computers, that this was going to be a mainstream memory for data centers in the future.

And at first it sounded ridiculous, but several of the CEOs believed me and decided to invest in building HBM memories.

Another memory was rather odd to put into a data center is the low-power memories that we use for cell phones.

And we wanted them to adapt them for super computers in the data center.

And they go cell phone memory for super computers and I explained to them why.

Well, look at these two memories, LPDDR5, HBM4, the volumes are so incredible.

All three of them had record years in history and these are these are 45-year companies.

And so, you know, that's part of my job is to inform and shape, inspire, you know.

So you're not just manifesting the the future and maybe inspiring in video, the different engineers of the company year, you're manifesting the supply chain of the future.

So you're having conversations with ESMC with ASML upstream downstream, upstream downstream.

So that's the thing EV caterpillar.

Yeah, that's downstream from us.

Yeah.

Yeah.

Yeah.

Yeah.

The whole thing.

I mean, but that's so there's so much incredibly difficult engineering that happens.

And the entire semiconductor industry and it just feels scary how intricate the supply chain is, how many components there are, but it works somehow.

Exactly.

The deep science, the deep engineering, the incredible manufacturing and so much of the manufacturing is already robotics, but we have a couple of hundred suppliers that contribute the technology that goes into our 1.3 million component rack.

Each rack is 1.3, one and a half million components.

They're 200 suppliers across the very room in rack.

So essentially, that you don't list that as a thing that keeps you up at night in the list of blockers.

But I'm doing, I'm doing all the things necessary, too.

Okay.

Do you see?

I can go to sleep because I checked it off.

I said, okay, you know, I go, I can go to sleep, but I go, well, let's see, let's reason about this.

What's important for us?

Because, okay, let's reason about this.

Because we changed the system architecture from the original DGX1 that you remembered to embelling 72 rack scale computing.

What does that mean?

What does that mean to software?

What does that mean to engineering?

What does that mean to how we design and test?

And what does that mean to the supply chain?

Well, one of the things that meant was we moved super computer integration at the data center into super computer manufacturing in the supply chain.

If you're doing that, you also have to recognize you're going to move and, and if, if you're, if you're, you know, total footprint of whatever data center you're going to build, let's say you would like to have, you know, 50 gigawatts of super computers that are running simultaneously.

And it takes one week to manufacture that 50 gigawatts of super computers.

Then each week in the supply chain, the super computers are going to need a gigawatt of power.

And so, so we're going to need the supply chain to increase the amount of power it has to build, test, to build and test the super computers in the supply chain before I ship it.

Well, embelling 72 literally build super computers in the supply chain and ships them two, three tons at a time per rack.

It used to be, they used to come in parts and we used to assemble them inside data center.

But that's impossible now because I'm reeling 72 so dense.

And so, that's an example.

And I would have to go into, you know, I've fly into the supply chain, go meet my partners and hey, I said, guess what?

So here's what we're going to do with, this is the way we used to build our DJ access.

We're going to build in this way.

This is going to be so much better because we're going to need them for inference, the market for inferences, you know, coming the inflation point for inferences coming it's going to be a big market.

That's why I first explained to them what's going on, why it's going to happen.

And then I asked them to make several billion dollars of capital investments each.

And because they, you know, they trust me and I'm very respectful of them and I, I give them every opportunity to question me and I spent time to explain things to people and I reasoned about it.

I draw on pictures.

And I reasoned about it in first principles.

And by the time I'm done with them, there's no way to do.

So it's a lot of it's about relationships and building a shared view of the future.

But do you worry about certain bottlenecks?

I mean, what are the bigs of bottlenecks in the supply chain?

Are you worried about it?

Is someone's a V tooling?

Are you worried about the packaging of GSMC about how fast the scale?

Like you said, you're not only growing incredibly fast, you're accelerating a growth.

So it, it feels like everybody in supply chain and those are certainly bottlenecks would have to scale up.

Are you having conversations with them?

Like how can you scale up the faster?

Do you worry about it?

No.

Okay.

Because because I told them what I needed, they understood what I need.

They told me what they're going to go do.

And I believe that what they're going to do.

Interesting.

Yeah.

That's great to hear.

So maybe if we can just linger in the power for a little bit, what are your hopes for how to solve the energy problem?

One of the areas likes that I'm, that I would love, I would love us to talk about and just get the message out.

You know, our, our, our power grid is designed for the worst case condition with some margin.

Well, 99% of the time, we're nowhere near the worst case condition because the worst case condition is a few days in the winter, a few days in the summer and extreme weather.

Most of the time, we're nowhere near the worst case condition and we're probably running around, call it 60% of peak.

And so 99% of the time, our power grid has excess power and they're just sitting idle.

But they have to be there sitting idle because just in case when the time comes, hospitals have to be powered and, you know, infrastructure has to be powered and airports have to run and so on and so on and so forth.

And so the question that I have is whether we could go and help them understand and create contractual agreements and design computer architecture systems, data centers such that when they need the maximum power for infrastructure and society that the data centers would get less.

But that's in a very rare instance, anyways.

And during that time, we either have back up generators for that little part of it or we just have our computers shift to workload somewhere else or we have the computers just run slower.

You know, we could degrade our performance reduce our power consumption and provide for, you know, slightly longer latency response, you know, when somebody asks for, you know, ask for an answer.

And so I think that that that way of using computers of building data centers instead of expecting a hundred percent up time and these contracts that are really, really quite rigorous, it's putting a lot of pressure on the grid to be able to now they're going to have to increase from their maximum.

I just want to use their access.

I just sit in there.

Yes, I thought talked about enough.

So what's stopping there is a regulation is it bureaucracy?

I think it's it's a throughway problem.

It starts with the end customer.

The end customer puts, puts requirements on the data centers that they can never not be available.

Okay, so that the end customer expects perfection.

Now, in order to deliver that perfection, you need a combination of backup generators and your grid power supplier to deliver on perfection.

And so everybody's got to have six nights.

Well, I think first of all, right now, we ought to have everybody understand that when the customer asks for these things, you got somebody, you have somebody in your data center operations team disconnected from the CEO.

I bet the CEO doesn't know this.

I'm going to talk to all the CEOs.

The CEOs are probably not paying any attention to the contracts that are being signed.

And so everybody wants to sign the best contract of course.

And they go down to the cloud service providers and the contract, the two contract negotiators that are, I could just see them now, you know, negotiating these multi-year contracts, both sides want, you know, the best contract as a result.

The CSPs then have to go down to the utilities and they expect the nine to six nights.

And so I think, I think the first thing is just make sure that all of the customers, the CEOs of the customers realize what they're asking for.

Now, the second thing is we have to build data centers that gracefully degrade.

And so if the power, if the utility of the grid tells us, listen, we're going to have to back you down to about 80 percent.

We're going to say, that's no problem at all.

We're just going to move our workload around when to make sure that data is never lost.

But we can reduce the computing rate and use less energy, the quality of service degrades a little bit.

For the critical workloads, I shift that somewhere else right away.

So I don't have that problem.

And so, you know, whoever, whichever data center still has a hundred percent uptime.

And so how difficult of an engineering problem is that the smart dynamic allocation of power in the data center?

As soon as you could specify, you could engineer it.

So long as it obeys the laws of physics on first principles, I think we're good.

What was the third thing you were mentioning?

So the second thing is the data centers.

And the third thing is we need the utilities to also recognize that this is an opportunity.

And instead of instead of saying, look, it's going to take me five years to increase my grid capability.

If you, if you have, if you're willing to take power of this level of guarantee, I can make them available for you next month.

And at this price.

And so if utilities also offered more segments of power delivery promises, then I think everybody will figure out what to do with it.

But there's just way too much waste in the grid right now, which we should go after it.

You've highly lot of Elon's and XAS accomplishment in Memphis in building classes, supercomputer, probably in record time in just four months.

It's now at 200,000 GPUs and growing very quickly.

Is there something that you could speak to the understand about his approach?

That's instructive to the broadly to all the data center creators that enable that kind of accomplishment.

He's approached the whole management of construction everything.

First of all, Elon is deep in so many different topics.

Yet he's also a really good systems thinker.

And so he's able to think through multiple disciplines.

And he obviously pushes things, questions, everything.

Where is there number one?

Is it necessary?

Number two, does it have to be done this way?

And it doesn't have to take this long.

And so he has the ability to question everything to the point where everything is down to its minimal amount, that's necessary.

You can't take anything else out.

And yet the necessary capabilities of the product retains.

And so he is as minimalist as you could possibly imagine.

And he does it at a system system scale.

I also love the fact that he is represented.

He is present at the point of action.

He was just there.

And there's a problem.

He'll just go there and show me the problem.

When you do all of this in combination, you overcome a lot of previous decisions that's the way we do it.

I'm waiting for them.

Everybody has a lot of excuses.

And then the last thing is when you act personally with so much urgency, it causes everybody else to act with urgency.

And every supplier has a lot of customers going on.

Every supplier has a lot of projects going on.

And he makes it his business that he's the top priority of everybody else's projects.

And so he does that by demonstrating.

Yeah, I've been in a bunch of those meetings.

It's fun to watch because really, not enough people ask the question like, okay, so can this be done a lot faster and how why does that have to take this long?

Yeah.

And then that becomes an engineering question often.

And yes, I think when you get the ground truth of actually, remember one of the times that's hanging out with him, he literally is going through the entire process of how to plug in cables into a rack.

He's working with a engineer on the ground that's doing that task.

And he's just trying to understand what does that process look like.

So it can be less air prone and just building up that intuition from every single task involved and putting together the data center.

You start to immediately get a sense at the detailed scale and then at the broad system scale of where the inefficiencies are.

So you can make it more and more and more efficient.

Plus you have the big hammer of being able to say, let's do it totally different.

Yeah.

And let's move all possible blockers.

That's right.

Is there parallels in the Nvidia extreme systems code design approach?

Do you see in the way Elon approaches systems engineering?

Well, first of all, the code design is a ultimate systems engineering problem.

Yeah.

And so we approach the work that we do from that first from that principle.

The other thing that we do, and this is a philosophy that I thought a state of mind, I guess, a method that I started 30 years ago and it's called the speed of light.

A speed of light is not just about the speed of speed.

It might shorthand for what's the limit of what physics can do.

And so every single, everything that we do is compared to the speed of light.

Memory speed, math speed, power, cost, time, effort, number of people, manufacturing cycle time.

And when you think about latency versus throughput, when you think about cost versus throughput, cost versus capacity, all of these things, you test against the speed of light to achieve all of these different constraints separately.

And then when you consider it together, you know you have to make compromises because a system that achieves extremely low latency versus a cheap, a system that achieves their high throughput are architected fundamentally differently.

But you want to know what's the speed of light of a system that achieves high throughput, what's the speed of light of a system that achieves low latency.

And then when you think about the total system, you can make tradeoffs.

And so I force everybody to think about what's this, what the first principles, the limits, the physical limits.

For everything before we, you know, before we do anything.

And we test everything against that.

And so that's a good frame of mind.

I don't love the other methods, which is continuous improvement.

The problem with continuous improvement, first of all, you should engineer something from first principles at the speed, you know, with speed of light thinking, limit it only by physical limits and physics limits.

And after that, of course, you would improve it over time.

But I don't like going into a problem.

And somebody says, hey, you know, it takes 74 days to do this today right now.

And we can do it for you in 72 days.

You know, I rather strip it all back to zero.

And so first of all, I explained to me why it's 74 days in the first place.

And let's think about what's possible today.

And if I were to build it completely from scratch, you know, how long would it take oftentimes you'd be surprised and might come to six days.

Now the rest of the six days to 74 could be very well-reasoned and compromises and, you know, cost reductions and all kinds of different things.

But at least you know what they are.

And then now that you know that six days possible, then the conversation from 74 to six, surprisingly, much more effective.

And such incredible complex systems that you're working with is simplicity sometimes a good heuristic to reach for.

I mean, if I can just, I mean, the pod, the very Ruben pod that you know, is just incredible.

We're talking about seven chips, seven chip types, five purpose build rack types, 40 racks, 1.2 quadrillion transistors, nearly 20,000 video dies.

Over 1100 Ruben GPUs, 60 x of flops, 10 petabytes per second of scale, bandwidth.

That's all just one.

That's just one pod.

That's just one pod.

Yeah, that's just one pod.

I mean, so you have the, and then even the Dan V also need to rack alone is 1.3 million components, 1300 chips, 4,000 pounds crammed into a single, 19-inch wide rack.

And Lex, we're probably kind of crank out about 200 of these pods a week just to put in perspective.

The amount of different components as supposed simplicity is impossible, but is that a metric that you kind of reach for and trying to design things?

You know, the phrase that I use most often is we need things to be as complex as necessary, but as simple as possible.

And so the question is, is all that complexity there necessary?

And we ought to test for that.

And we ought to challenge that.

And then after that, everything else above it, you know, is gratuitous.

But some of the most incredible, semiconductor industry broadly, but what Nvidia is doing, some of the greatest engineering in history, so these systems are just truly, truly marvels of engineering.

It is the most complex computer the world has ever made.

Yeah, the engineering teams, I mean, it's not a competition, but I don't know.

If it was like an Olympics of engineering teams in the TSMC, it doesn't credible engineering.

Like I said, ASML at every scale, but Nvidia is going to give them a run for their money.

Yeah.

Just incredible, this is the team's gold medalists in every single sport, all assembled right here and have to work together and report directly to you.

This is wonderful.

You've recently traveled to China.

So, it's interesting to ask you, China's been incredibly successful in building up a technology sector.

What do you understand about how China is able to over the past 10 years build so many incredible world-class companies, world-class engineering teams, and just this technology ecosystem that produces so many incredible products?

A whole bunch of reasons.

Well, first of all, let's start with some facts.

50% of the world's AI researchers are Chinese, plus or minus, and they're mostly in China.

Still, we have many of them here, but there's amazing researchers still in China.

They're tech industry showed up at precisely the right time.

At the time of the mobile cloud era, their way of contributing with software, and so this is a country's incredible science amount, really well-educated kids.

Their tech industry was created during the era of software.

They're very comfortable with modern software.

China is not one giant economic country.

It's got many provinces in cities with mayors all competing with each other.

The reason why there's so many EV companies, that's the reason why there's so many AI companies, that's the reason where there's so many companies you can imagine, they all create some of them.

As a result, they have insane competition internally.

What remains is an incredible company.

They also have a social culture where it's family-first, friend-second, and company-third.

The amount of conversation that goes back and forth between essentially open source all the time.

So the fact that they contribute more to open source is so sensible because they're probably, what are we protecting?

You know, engineers, the brothers, they're in that company, their friends are in that company, and they're all schoolmates, you know, the schoolmate concept.

It's a, you know, once schoolmate, your brother for life, and so they share knowledge very, very quickly.

And so there's no sense keeping technology hidden, you might as well put it on open source.

And so the open source community then amplifies, accelerates the innovation process.

So you get this rapid, incredible, great talent, rapid innovation because of open source, and just, you know, the nature of friends.

And in saying competition, among the company, what emerges is incredible stuff.

And so this is the fastest innovating country in the world today.

And this is something that has everything that I just said is fundamental to just how the kids were grown.

The fact that they have excellent education, the fact that they parents want them to do once school, the fact that they, their cultures that way, these are, you know, these are just the thing about their country.

And they showed up at a precisely the time when technology is going through that exponential.

Plus culturally, it's pretty cool to be an engineer.

It connects to all the components that you're mentioning.

It's a, it's a builder nation.

It's a builder nation.

Yeah, it's a builder nation.

Our country's leaders incredible, but they're mostly lawyers.

Their country's leaders, and because we're, they're trying to keep us safe, rule of law governing their country was built out of poverty.

And so most of their leaders are incredible engineers.

Some of the brightest minds.

To take a small tangent, because you mentioned open source, I have to go to Proplexity here who you have been a fan of a long time.

I love it, you know, and thank you for releasing open source, Neumatron III super, which you can also use inside Proplexity looks stuff up now, which is 120 billion parameter, open weight, MOE model.

What's your vision with open source?

So you mentioned China with, with deep seek and many max with all these companies really pushing forward the open source AI movement, and Nvidia is really in the way in close to state of the art, open source LLMs.

What's your vision there?

First, if we're going to be a great AI computing company, we have to understand how AI models are evolving.

One of the things that I love about Neumatron III is it's, it's not just a pure transformer model, it's transformer and SSMs.

And we were early in developing the conditional GANS, which that progressive GANS, which led step-by-step to diffusion.

And so the fact that we're doing basic research in model architecture and in different domains gives us visibility into what kind of computing systems would do a good job for future models.

And so it is part of our extreme co-design strategy.

Second, I think we rightfully recognize that on the one hand, we want world-class models as products, and they should be proprietary.

On the other hand, we also want AI to diffuse into every industry and every country, every researcher, every student, and if everything's proprietary, it's hard to do research and it's hard to innovate on top of around with.

And so open source is fundamentally necessary for many industries to join the AI revolution.

Nvidia has the scale and we have the motives to not only skills, scale, and motivation to build and continue to build these AI models for as long as we shall live.

And so therefore we ought to do that.

We can open up, we can activate every industry, every researcher, you know, every country to be able to join the AI revolution.

There's the third reason, which is from that to recognizing that AI is not just language, these AI's will likely use tools and models and subagents that were trained on other modalities of information.

Maybe it's biology or chemistry or, you know, laws of physics or, you know, fluids and thermal dynamics and not all of it is in language structure.

And so somebody has to go make sure that whether prediction, biology, AI, AI for biology, physical, AI, all of that stuff stays can be pushed to the limits and pushed at a frontier.

We don't build cars, but we want to make sure every car company has access to great models.

We don't, we don't discover drugs, but I want to make sure that Lily has the world's best.

Biology, AI systems so that they can go use it for discovering drugs.

So these three fundamental reasons, both in recognizing that AI is not just the language that AI is really broad, that we want to engage everybody into the world of AI and then also co-design it.

Well, I have to say, once again, thank you for open sourcing.

It's really truly open sourcing, and you're trying to bring in.

Yeah, I appreciate you for saying that we open source the models, we open source the weights, we open source the data, we open source how we created it.

Yeah, it's really incredible.

You're originally from Taiwan and have a close relationship with TSMC.

So I have to ask TSMC, I think, also is a legendary company in terms of the engineering teams in terms of the incredible engineering work that they do.

What do you understand about TSMC culture and their approach that explains how they're able to achieve this singular unmatched success in everything they're doing with semiconductors?

You know, first of all, the deepest misunderstanding about TSMC is that their technology is all they have.

That somehow, they have a really great transistor, and if somebody shows up another transistor game over, it's the technology, and of course, you know, I don't mean just the transistor, middleization systems, the packaging, the 3D packaging, the silicon photonics that, you know, all of the technology that they have, that technology is really what makes the company special, their technology makes the company special.

But their ability to orchestrate the demand, the dynamic demands of hundreds of companies in the world as they're moving up, shifting out, you know, increasing, decreasing, pushing out, pulling in, changing from customer to customer.

Way for starting, way for stopping, emergency, way for starts, you know, all of this dynamics of the world's complexity as the world is shaped, shifting all the time, and somehow they're running a factory with high throughput, high yields, really great cost, excellent customer service.

They take their work, they take their promise to seriously, when you're wafered, because they know that you're helping you run your company, when the wafer's were promised to show up, the wafer's show up, you know, so that you could run your company appropriately.

So there's system, there's manufacturing system is completely miraculous.

I would say then the second thing is their culture.

This culture is a simultaneously, technology focused on one hand, advancing technology, simultaneously customer service oriented on the other hand.

A lot of companies are very customer service oriented, but they're not very technology, excellent.

They're not at the bleeding as your technology or a lot of companies who are at the bleeding as your technology, but they're not the best customer service oriented company.

And so it just depends on somehow they've balanced these two and their world class of both.

And then probably the third thing is the technology that I most value in them, that they created this, you know, this, this, this, uh, intangible cost trust, I trust them to put my company on top of them.

That's a very big deal.

Well, they trust them.

I mean, there's a really close relationship there, the of established and that trust is established based on many years of performance, but there's human relationships involved there as well.

Three decades.

I don't know how many tens, hundreds of billions of all of the business we've done through them.

And we don't have a contract.

That's pretty great.

That's amazing.

Okay.

There's a story that in 2013, the founders of TSMC, Morris Chang offered youth a chance to become TSMC chief executive.

And you said you already had a job.

This story true, story is true.

I didn't dismiss it, but I was deeply honored.

And of course, of course, I knew then as I know now TSMC is one of the most consequential companies in history.

Yeah.

And Morris is one of the the highest regarded executive and and business and personal friend that I've that I've had in my life.

And for him to ask is, I was humbled and really honored.

But the work that I'm doing here is really important.

And I've seen, you know, in my mind in your ways and my mind's eye, what in video was going to be and what the impact that we could have.

And it was really important work.

And it's my responsibility, you know, my sole responsibility to make this happen.

And so I, I, I, I declined it, you know, not not because it wasn't an incredible offer.

It is an unbelievable offer, but I simply couldn't take it.

I think in video, both in video and TSMC are two of the greatest companies in the history of human civilization and running either one.

I'm sure is incredibly complicated effort and takes you have to truly be all in, you know, everybody every scale, not just at the CEO level, everybody is really truly all in, you know, to accomplish this kind of complexity.

So you know, I can help both companies exactly.

So in video is now the most valuable company in the world, I have to ask what is the Nvidia's biggest vote as the folks in the tech sector say, the edge you have that protects you from the competition.

Our single most important property as a company is the install base of our computing platform.

Our single most important thing is the end, today is our is the install base of CUDA.

Now the reason why 20, 20 years ago, of course, there was no install base.

But what makes, and if somebody, if somebody came up with with a CUDA or CUDA, it wouldn't make any difference at all.

And the reason for that is because it's never been just about the technology to technology, of course, was incredible visionary.

But it's the fact that the company was dedicated to what stuck with it, expanded its reach.

It wasn't three people that that made CUDA successful.

It was 43,000 people that made CUDA successful.

And there's several million developers that believed in us that trusted that we were going to continue to make CUDA 1, 2, 3, 13, that they decided to port and dedicate their software on top of it, their mountain of software on top of it.

And so the install base is the number one most important advantage that install base when you amplify it with the velocity of our execution.

At the scale that we're talking about, no company in history had ever built systems of this complexity period.

And then to build it once a year is impossible.

And and that velocity combined with the install base in the developers mind, it's just going to now take a developers mind from the developers perspective.

If I support CUDA tomorrow it will be 10 times better.

I just have to wait six months on average.

Not only that, if I develop it on CUDA, I reach a few hundred million people computers.

I'm in every cloud.

I'm in every computer company.

I'm in every single industry.

I'm in every single country.

So if I decreated an open source package and I put it on CUDA 1st, I get these built attributes simultaneously.

And not only that, I trust 100% that Nvidia is going to keep CUDA around and maintain it and improve it and keep optimizing the libraries for as long as they shall live.

You could take that to the bank and that last part trust.

You put all that stuff together.

If I were a developer today, I would target CUDA 1st.

I would target CUDA most.

And that's the reason that that I think in the final analysis is our first, that's even our first core advantage.

Our second one is our ecosystem.

The fact that we vertically integrated this incredibly complex system, but we integrated horizontally into every single, every single company's computers, we're in the Google Cloud.

We're in Amazon.

We're in Azure.

We're ramping up AWS like crazy right now.

We're in new companies like core weave and end scale.

We're in super computers that lily.

We're in enterprise computers.

We're at the edge in radio base stations.

You know, I mean, it's just crazy.

One architecture isn't all these different systems.

We're in cars.

We're in robots.

We're in satellites.

We're out in space.

And so the fact that you have this one architecture in the ecosystem is so broad.

It basically covers every single industry in the world.

Well, how does the CUDA install base evolve into the future with AI factories as a mode?

Do you think it's possible that in the video the future is all about the AI factory?

Well, the unit of computing used to be GPU to us.

Then it became a computer.

Then it became a cluster.

Now it's an entire AI factory.

When I see a computer, when I see what in video builds, in the old days, I would, you know, I visualize the chip.

And then when I announced a new product, you know, a new generation like ladies and gentlemen, we're announcing amp here today.

I pick up the chip.

Yeah.

That was my mental model.

What I was building today.

I don't, I went picking up the chip is kind of still adorable.

But it's adorable.

It's not, it's not my mental model of what I'm doing.

My mental model is this giant gigawatt thing that has power generation is connected to the grid.

It's got cooling systems and networking of incredible, much-strossity.

You know, 10,000 people are in their trying to install it hundreds of networking engineers in their thousands of engineers behind it trying to power it up.

You know, powering up one of those factories, as you know, it's not somebody going, it's on now.

It takes thousands of people to bring it up.

So mentally, you're actually, when you're thinking about a single unit of compute, you're like literally, when you go to bed night, you're thinking now about collection of racks, so pods, not individual chips.

And I'm hoping my next click is when I'm thinking about building computers, it's, you know, planetary scale.

That'll be the next click.

What do you think about the space angle that Elon has talked about in doing compute in space for solving some of the it makes some of the energy issues in terms of scaling energy easier.

Cooling issues is not easy.

You know, cooling, well, there's a large number of engineering complexities in all of that.

So what, you know, Nvidia has also announced that you're already thinking about that.

Yeah, we're already there.

Nvidia GPUs are the first GPUs in space.

And I didn't realize it was, it was so interesting to, I would have declared it maybe more in space, you know, little, little astronaut suit on one of our GPUs.

But we've been in space.

It's the right place to do a lot of imaging, you know, because those satellites have really high resolution imaging systems.

And they're sweeping the earth, you know, continuously now.

And you want, you know, centimeter scale, you know, imaging that has done continuously for the world.

So, you know, you know, you basically have real time telemetry of everything.

You don't want to beam that back down to earth.

It's just, you know, petabytes and petabytes of data.

You have to just do AI right there at the edge.

Throw away everything you don't need.

You've seen before, didn't change.

And then just keep the stuff that the uni.

And so AI had to be done at the edge.

Obviously, we have 24, 7 solar if we put it at the Polars.

And, you know, there's no conduction, no convection.

And so, you know, you're pretty much just radiation.

And, but, you know, space is big.

I guess, you know, we're just going to put big giant radiators out there.

How crazy am an idea do you think it is?

Like, is this, is this five years out, 10 years out, 20 years out?

So, we're talking about blockers for AI scaling.

You know, just so much more practical.

I look for where my next bucket of opportunities are first meanwhile, I'm cultivating space.

And so, I sent engineers to go work on the problem.

We're starting, we're learning a lot about it.

How do we do a radiation?

How do we do a degrading performance?

How do we deal with continuous testing and attestation of defects?

And, you know, how do we do a redundancy?

And how do we degrade gracefully and things like that?

And so, what about software?

How do you think about software and redundancy and performance out in space?

Make it so that the computer never breaks, it just gets slower.

And so, we could start doing a lot of engineer exploration upfront.

But in the meantime, my favorite answer is eliminate waste.

You know, we've got all that idle power.

I want to evacuate it as fast as possible.

Yeah, there's a lot of low hanging food here on Earth that we can utilize for the AI scaling quick pause.

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She's wise in my friends.

And now, back to my conversation with Jensen Kuala.

Do you think Nvidia may be worth 10 trillion at some point?

Let's let's ask it this way.

What is the future of the world look like where that's true?

I think that Nvidia's growth is extremely likely and in my mind inevitable.

And let me explain why.

Or the largest computer company in history.

That alone should beg the question why.

And the reason for course, two reasons.

First, the two foundational technical reasons.

The first reason is that computing went from being a retrieval based file retrieval system.

Almost everything is a file.

We pre-write something, we pre-record something.

We draw something, we put it on the web, we put it on a file.

And we use a recommended system, some smart filter, to figure out what to retrieve for you.

And so we were pre-recording human pre-recording and file retrieving system.

That's what a computer is, largely.

To now, AI computers are contextually aware, which means that it has the process and generate tokens in real time.

So we went from a retrieval based computing system to a generative based computing system.

We're going to need a lot more processing in this new world than in the old world.

We need a lot of storage in the old world.

We need a lot of computation in this new world.

And so that's the first part of it that we fundamentally changed computing in the way how computing is done.

The only thing that would cause it to go back is if this way of computation, this way of computing generating information that's contextually relevant, situationally aware that is grounded on new insight before it generates information.

This computation intensive way of doing computing would only go back if it's not effective.

So for the last 10, 15 years while working on deep learning, if at any single moment, I would have come to the conclusion that that, you know what, this is not going to work out.

I think this is a dead end or it's not going to scale.

It's not going to solve this modality.

Not going to be used in this application.

Then of course, I would feel very differently about it.

But I think the last five years has given me more confidence than the last 10 years, the previous 10 years, the second idea is computers because it was a storage system.

It was largely a warehouse.

We're now building factories.

Warehouses don't make much money.

Factories directly correlates with the company's revenues.

And so the computer did two things.

Not only did it change the way it did it.

It's purpose in the world changed.

It's no longer a computer, it's a factory.

It's a factory is used for generation of revenues.

We now see not only is this factory generating products, commodities that people want to consume, we're seeing that the commodities are so interesting, so valuable, so to so many different audiences that the tokens are starting to segment like iPhones.

You have a free tokens, you have premium tokens, and you have several tokens in the middle.

So intelligence, as it turns out, is a scalable product.

There's extremely high intelligence products, tokens that are used for specialized things.

People will be willing to pay the idea that somebody is willing to pay $1,000 per million tokens.

It's just around the corner.

It's not if.

It's only when.

And so now we're seeing that the commodity that this factory makes is actually valuable and is revenue generating and profit generating.

Now the question is how many of these factories can does the world need?

How many tokens does the world need?

And how much is society willing to pay for these tokens?

And what would happen to the world's economy?

If the productivity were to improve so substantially, what would happen?

Are we going to discover new drugs, new products, new services?

And so when you take these things in combination, I am absolutely certain that the world's GDP is going to accelerate and growth.

I'm absolutely certain the percentage of that GDP that will be used for computation will be 100 times more than the past because it's no longer a storage unit.

It's a product generation unit.

And so when you look at it in that context and then you back into what is Nvidia?

What does Nvidia do?

And how much of that new economics, new industry, would we have to benefit to address?

I think we're going to be a lot lot bigger.

And then the rest of it, to me, is it possible for Nvidia to be a $3 trillion revenue company in the near future?

The answer is of course yes.

And the reason for that is because it's not limited by any physical limits.

There's nothing that I see that says, you know, gosh, $3 trillion is not possible.

And as it turns out Nvidia supply chain is the burden is shared by 200 companies.

And the fact that we scale out on the backs of what the partnership of this ecosystem, the question is do we have the energy to do so?

And surely we will have the energy to do so.

And so all of these things combined, that number is just a number.

And I still remember Nvidia was a, Nvidia was a, the first time we crossed a billion dollars.

I was reminded of a CEO who told me, you know, Jensen is theoretically impossible for a fabulous semiconductor company to exceed a billion dollars.

And, and I won't bore you with why, but, but the, of course, is a logical and there's a lot of evidence.

We're not.

And then that somebody told me, you know, Jensen, you'll never be more than $25 billion because of some other company.

Somebody told me that you'll never be, you know, because, and then said, so that those, those aren't principal, first principal reason thinking and the simple, the simple way to think about that is, what is it that we make and how large is the opportunity that we can create.

Now Nvidia is not in the market share business.

Almost everything that I just talked about don't exist.

That's the part that's hard.

You know, if Nvidia was a, was a, was a $10 billion company trying to take Nvidia share, then it's easy to see for shareholders that, oh yeah, if they could just take 10% share, they could be this much larger.

But it's hard for people to imagine how large we could be because there's nobody I could take share from, you know, and so, so I think that that's one of the challenges for the world is the imagination of the future.

But I got plenty of time and I'll keep reasoning about it and I'll keep talking about it and every single GTC will become more and more real, you know, and then more and more people talk about it in one of these days, you know, we'll get there, but 100% will get there.

Yeah, this view of, you know, token factories essentially, this token per second for what, and every token having value, like it's an actual thing that brings value and it brings different kinds of value, different amounts of value to different people, that's the actual product.

This really could be loosely thought of as the token, and so you have a bunch of token factories, and it's very easy.

First principles to imagine a future, given all the potential things that AI can solve, that you're going to need an exponential number more of token factories.

Yeah, and what's really interesting, the reason why I'm so excited about it, the iPhone of tokens arrived.

What do you call it?

Wait, are you saying Apple Closet iPhone?

Yeah, that's interesting.

Agents.

Yeah, agents.

True.

Agents in general.

The iPhone of tokens arrived.

It is the fastest growing application in history.

It went straight up.

Yeah.

Went straight up.

That says something.

Yep, there's no question.

Open Closet is the iPhone of tokens.

Yeah, there's something truly, as you know, something truly special happening from about December.

Where people really woke up to the power of cloud code of codex, of open-cloth.

I mean, I've embarrassed to admit that in the way here in the airport, I'm, it's first time I've done this in public, I was programming code on quote, by talking, and I was embarrassed because I was pretending like I'm talking to a human colleague.

I'm not sure how I feel about the future where everybody is walking around talking to their AI, but it's such an efficient way to get stuff done.

And it's more likely that your AI is bothering you all the time.

And the reason for that is because it's getting stuff done so fast.

Yeah.

It's reporting back to you.

I got that done.

You know, what do you want me to do next?

You know, that's the part that I think most people don't realize is the person who's going to be chatting with them texting them most, is there, is there clause or lobster?

What an incredible future.

I read the YouTube a lot of your success to your ability to work harder than anyone and withstand more suffering than anyone.

So we can list many of the things that entails.

I mean, dealing with failure, the cost of engineering problems we've talked about, the human problems, uncertainty, responsibility, exhaustion, embarrassment, the near-death company moments that you've mentioned.

But also the pressure.

Now is the CEO of this company that economies and nations strategize around plan their financial locations around plan their AI infrastructure around.

How do you deal with this much pressure?

Well, gives you strength given how many nations and peoples depend on you.

I'm conscious about the fact that Nvidia success is very important in the United States.

We generate enormous amounts of tax revenues.

We establish technology leadership for our nation.

Technology leadership is important for national security.

National security not just in one aspect of national security, all aspects of national security.

When our country's more prosperous, we could do a better job with domestic policies and helping social, social benefits.

Because we're generating so much rein industrialization in the United States, we're creating amounts of jobs.

We're helping shift how we build things back to the United States in so many different plants, chips, computers, and of course these air factories.

I'm completely aware that I have the benefit and this is a real, real, a real gift with mainstream investors, teachers, policemen who have somehow, for whatever reason, invested in Nvidia or because they watched Jim Cramer, bought some stock and now are millionaires, and I am completely aware of that circumstance.

I'm aware of the circumstance that that Nvidia is central to a very large network of ecosystem partners behind us and downstream from us.

So the way I deal with that is exactly what I just did.

I reason about what is it that we're doing?

What is it causing?

What's the impact that has on other people benefit in a positively or even through great burden, for example, to supply chain?

And the question is, therefore what are you going to do about it?

And almost everything that I feel, I break it down, I reason about, okay, what's the circumstance?

What is, what has changed, what's hard, and what am I going to do about it?

And I break it down, decompose the problem, and the decomposition of these circumstances turns it into manageable things that I can do.

And the only thing that I after that I could do is, did you do it?

Did you either do it?

Or did you get somebody else to do it?

And if you didn't do it, you reason that you need to do it, and you didn't do it, and you didn't get anybody else to do it, then stop crying about it.

And so I'm fairly tough on myself.

But I also break things down so that I don't panic.

I can go to sleep because I've made the list of things that needed to be done.

And I've made sure that everything that could put our company in harm's way, could put my partners in harm's way, put our industry in harm's way, I've told somebody.

Everything that I feel could put anybody in harm's way, I've told someone.

And I've told that someone who could do something about it.

And so I've gotten it off my chest, or I'm doing something about it.

And so after that, Lex, what else can you do?

So given all the insane intense amount of suffering on the journey of building up in video, you have you hit low point psychologically?

Oh, yeah.

Oh, yeah, sure.

All the time.

All the time.

And there's just breakdown the problem, into pieces.

You know, you could do about it.

And part of, you know, Lex, part of it, part of it is forgetting.

One of the most important attributes of AI learning, as you know, is right systematic for getting.

You need to know when to forget something.

You can't memorize everything.

You can't keep everything.

And you know, you don't want to carry everything.

One of the things that I do very quickly is I decompose the problem.

I reasoned about the problem.

And I shared a load with it.

When I say, I tell everybody, I'm essentially sharing that burden.

Yeah.

That's quickly as possible.

Whatever worries me, tell somebody else.

Don't just keep it.

You know, decompose, don't, don't freak them out.

Decompose the problem into smaller parts and get people to, and inspire them to be able to go do something about it.

But part of it is just forgetting.

You know, a lot of it is, you've got to be tough on yourself.

You know, just come on, stop crying about it.

Let's get going.

You know, and then you get out of bed.

And then the other part is, is you, you're attracted to the next shiny light, the next future.

You know, the next opportunity, the next, okay, that's behind us.

Let's what's next.

And it's a lot, I think, you know, you watch this with great athletes.

They, they, and just worry about the next point.

The last point is behind them.

The embarrassment, you know, said back.

You know, and then, and because I do so much, my job publicly, you know, Lex, you do fair amount of your job publicly too.

And so, so I do a lot of my job publicly.

And so, you know, I, I say a lot of things that that seems sensible at the time, or funny at the, mostly it's just because it's funny to me at the time.

And then, you know, you've, reflect on it's less funny.

But, but, yeah, trust me, I know.

But you basically allow yourself to be pulled by the light of the future, forget the past and just keep.

That's right.

Keep working towards that.

I mean, you did say there's this kind of famous thing you said that, if you knew how hard it would be to build a video, it turned out to be, what is it a million times more hard than you anticipated?

Are they you wouldn't do it?

Yeah.

But it isn't, you know, when I hear that, that's probably true about everything worth doing, right?

Exactly.

That is by the way, what I was trying to explain is that there's a, there's a incredible superpower of being, um, being, being, uh, have the mind of a child, you know, and I say to myself often times when I look at something and, and almost, almost everything.

Um, my first thought is how hard can it be, you know, and so, and so you get yourself into that mode, how hard could it be?

And, and nobody's ever done it.

It looks gigantic.

It's going to cost hundreds of billions of dollars.

It's going to take, you know, all this, and you just go, yeah, but how hard could it be?

You know, yeah, how hard could it be?

Yeah.

And so, so you got to get yourself into that state of mind.

You don't want to, you don't want to actually over-simulate everything, and all the setbacks and all the trials and tribulations and all the disappointments.

You don't want to simulate all that in advance.

You don't want to know that.

You don't, you don't, you want to go into a new experience, thinking it's going to be perfect.

It's going to be great.

It's going to be incredibly fun.

And then while you're there, you know, you need to have, you need to have endurance, you need to have grit, so that when the setbacks actually happened, and those setbacks are going to surprise you, that this points out, this points out against surprise you, you know, the embarrassment's are going to surprise you, the humiliations are going to surprise you.

Um, you just can't, now you just got to turn on the other bit, which is just forget about it.

Move on, keep moving.

And, and to the extent that, to the extent that my assumptions about the future and why the future is going to manifest, so long as those assumptions in that input doesn't change or didn't change materially, then I should expect that the output won't change.

And so my simulated output of the future is still going to happen.

And if it's still going to happen, I'm still going to go after it.

I believe it's going to, you know, and so there's a combination of two or three human characteristics, the ability to go into an experience fresh-minded, the ability to forget the setbacks, the ability to believe in yourself, you know, to believe what you believe and stay true to that belief, but you're constantly evaluating.

This combination of three, four, five things, I think is is really important for resilience.

And, and, and, you know, I, I'm, I'm fortunate that that whatever, whatever life experiences led to this, I've got kind of those four or five things, you know, I'm always curious, always learning, I'm always learning from everybody, you know, I'm always asking my, and because I'm humble about, about everything, I'm always thinking, gosh, they did that so nicely, they did that so wonderfully, you know, I wonder what they're thinking through.

How do they, you know, so I'm simulating everybody.

In a lot of ways, you know, emulating almost everybody I watch, right?

You're, you're empathetic towards, towards everything that they do that, that you're observing and respecting.

And so you're constantly learning and, you know, you're now one of the wealthiest people on earth, one of the most successful humans on earth, is it harder to be humble and to be able to, do you feel the effect of money and power and fame in making it harder for you to sort of be wrong in your own head, enough to hear out an opinion of somebody else wanting to disagree with you and learn from them, those kinds of things.

Surprisingly, no, and I would, I would actually go the other way, because I do so much of my work publicly, when I'm wrong, pretty much everybody sees it, you get humbled, yeah, and, and when I'm wrong, when I'm wrong or it didn't turn out that way or, you know, I mean, most of the things that that I say outside, I'm fairly certain about, and the reason for that is because it's going to impact somebody else and I want to be quite concerned about that and quite, quite, quite circumspeck about that.

For stuff that that I'm reasoning about inside of meeting, you know, a lot of things could turn out differently.

And so, but it doesn't ever stop me from reasoning.

The way that, the way that I imagine lead, you know, I'm constantly reasoning in front of people and even when I'm talking to you, you can kind of see me kind of reasoning through things.

And I want to make sure that you understand what I'm saying not because I told you, because I'm so humbled about what I'm about to tell you, I kind of show you the steps that I got there, and then you could decide whether you believe what I said in the end.

And so, I'm doing that all day long in meetings with all of my employees, I'm constantly reasoning through, let me tell you how I see it, and I reasoned through it, it gives everybody the opportunity to intercept and say, I disagree with that part.

The nice thing about reasoning through things and letting people interact with it is that they don't have to disagree with your outcome.

They can disagree with your reasoning steps, and they could pull me in different directions, and then we can reason forward.

And so we're kind of, you know, collective path searching method and it's really fantastic.

Yeah, you have this way above you of when you're explaining stuff, I can feel you actually reasoning on the spot about it with a constant open mind in this, where you could, I could feel like I could steer your thinking.

Yeah, and that's a, that's really beautiful that you've been able to maintain that after so many years of success and pain.

I think sometimes pain makes you close, close you down a bit.

Yeah.

And I think you maintain tolerance for embarrassment because that's, that's the tolerance, I mean that's a real thing.

Yeah.

There's many years of embarrassing yourself, even those meetings, knowing that there's people around you where you declared one idea, and it was shown that that idea was wrong, and you able to admit that and to grow from that, that's not, that's very difficult on the human level.

Yeah.

Well, you know, the news that recently, my first job was, you know, cleaning toilets, so I'm glad you maintain that same spirit of Denny's, the work.

I mean, that was beautiful.

Your whole journey from starting from Denny's is a beautiful one.

Let me ask you about video games, some of a big gaming fan, you know, so I have to say thank you to the video for many years of incredible graphics.

By the way, it is GeForce is our still to this day.

Yeah.

Our number one marketing strategy.

Right?

People learn about Nvidia while they're in their teenage years.

And then they go to college and they know who Nvidia is.

And then in the beginning it's just, you know, playing Call of Duty, you know, you know, Fortnite.

And then later, they're using Kuda and then later, they're using Nvidia, you know, blender and just so when Auto does that.

I mean, I should say, I mentioned to a friend that I'm talking with you.

He said, oh, they make great gaming GPUs.

Yeah, exactly.

Exactly.

No, there's more to it.

But yeah, yeah, people really love the, it really brought a lot of joy to a lot of people.

The, the, the hardware really brings these worlds to life.

There is some controversy around this with the DLSS 5.

You know, can you explain to me the drama around this?

I guess people gamers online will concern that it makes games look like AI Slop.

Yeah.

What do you think of this drama?

Yeah.

I think their perspective makes sense.

And I can see where they're coming from.

Because I don't love AI Slop myself.

You know, all of it, the AI generated content increasingly looks similar and they're all beautiful.

And, and I can, so I can, I, I'm empathetic towards where they're, where they're thinking.

That's just not what DLSS 5 is trying to do.

I showed several examples of it.

But DLSS 5 is 3D condition, 3D guided.

It's ground truth, structure, data guided.

And so, so the artist determined the geometry.

We are completely truthful to the geometry maintained so and every single frame.

It's a condition by the textures, the artistry of the artist.

And so every single frame enhances, but it doesn't change anything.

Now, the question is, the question about enhancing.

DLSS 5 also lets, because the system is open, you could train your own models to determine, and you could even, in the future, prompt it.

You know, I wanted to be a tune shader.

I wanted to look like this kind of, you know, so you can give it even an example.

And it would generate in the style of that.

All consistent with the artistry, you know, the style, the intent of the artist.

And so, all of that is done for the artist so that they can create something that is more beautiful, but still in the style that they want.

I think that they got the impression that the games are going to come out the way the games are, ship the way they do, and then we're going to post-process it.

That's not what DLSS is intended to do.

DLSS is integrated with the artist.

And so it's about giving the artist the tool of AI, the tool of generative AI, they could design up the user, you know.

I think people are very sensitive to human faces.

Yeah.

And we're not living in this moment, which I think is a beautiful one, which is people are sensitive to AI Slop.

Yeah.

It puts a mirror to ourselves to help us realize that what we seek is imperfections, what we seek is sometimes not perfect graphics.

It helps us understand what we find compelling in the world will create.

And that's beautiful.

And as long as it's tools that help us create those worlds.

Yeah.

That's right.

That's right.

It's yet another tool.

And they want the generative models to generate the opposite of photo real.

Yeah.

It'll do that too.

And so it's just yet another tool.

I think the gamers might also appreciate that in the last couple of years we introduced skin shaders to the game developers.

And many of those games have skin shaders that include subsurface scattering that may skin look more skin-like.

And so the industries, the game developers are looking for more and more and more tools to express their art.

And so this is just yet more and more tool.

I think it's that with you.

Very good question.

What do you think is the greatest most influential game ever made?

Maybe from Nvidia's perspective.

Doom.

Doom.

Unquestionably, that was the start of the 3D.

I would say Doom from the intersection of the cultural implication as well as the industry turning a PC into a gaming device.

That was a very important moment.

Now, of course, flight simulation companies were before it.

But they just didn't have the popularity that Doom did to have made the industry turn the PC from an office automation tool into a personal computer for families and gamers and things like that.

Doom was really impactful there.

From an actual game technology perspective, I would say virtual fighter.

And so we're great friends both of them.

And then there's games more recently.

I mean, Cyberpunk 2077, really nice GPU accelerated graphics.

Like fully retraced, fully retraced.

Also, I like at person, I'm a huge fan of Skyrim, Elder Scrolls.

And it's been released a long long time ago, but people release mods.

It's like a different game.

It just allows me to replay the game over and over and it makes you realize that you can re-experience in a totally new way.

The world you already love.

So I let's do that all the time.

One of my favorites just walk on Skyrim.

We created this thing called RTX mod.

Uh-huh.

Yeah.

It's a modding tool.

And it allows it allows the community to inject the latest technology into an old game.

Of course, like what makes a great video again is not just graphics.

It's also story and care development.

But that's right.

Beautiful graphics can add to the immersion.

The feeling like it's another place that you're transported to.

What's uh, you've said, I think accurately, that the AGI timeline question rests on your definition of AGI.

So let me ask you about a possible timelines here.

Let's, this ridiculous definition, perhaps of what AGI is, but an AGI system that's able to essentially do your job.

So Ron, no, start, grow, and run a successful technology company.

That's worth a good one or A1.

No.

That's, that's to be worth more than a billion more, more than a billion dollars.

So, you know, you know, how hard it is to do all those components.

So how far we away from that?

So we're talking about open clock that does all the incredibly complex stuff that are required to, to first of all, innovate to find customers to sell to them to manage to build the team of some agents, some humans, all that kind of stuff.

Is this five, ten, fifteen, twenty years away?

I think it's now.

I think we've achieved AGI.

You think you get a company run by the AI system like this possible?

And the reason for that is us, you said a billion, and you didn't say forever.

And, and so for example, uh, it is not out of the question that, uh, a claw was able to create a web service, some interesting little app that all of a sudden, you know, a few billion people used for 50 cents.

And then it went out of business again shortly after.

Now we saw a whole bunch of those type of companies during the internet error.

And most of those websites were not anything more sophisticated than what open clock could generate today.

Actually, achieve virality and monetize that virality.

Yeah, it's just that I don't know what it is.

But I didn't, I couldn't have predicted any of those companies at the time either, you know?

You're going to get a lot of people excited with that statement.

Yeah, I know.

It's like, what do you mean?

I'm going to just launch an agent and make a lot of money.

Well, by the way, it's happening right now, right?

You know that when you go to China, you're going to see, you're going to see a whole bunch of people teaching the, getting their claws to try to go out and look for jobs and, you know, do work, make money.

And, and I'm not, I'm not actually, I wouldn't be surprised if some social thing happened or somebody created a digital influencers super, super cute or some social application that, you know, featured little Tomagachi or something like that.

And, and it become, and out of the blue, and instant success, a lot of people use it for a couple of months and it kind of dies away.

Now, the odds of, of, you know, 100,000 of those agents, building in video, as 0% and, and then, and then the, the one part that I will, I won't do, and I, and I, I want to make sure we all do, is to recognize that people are really worried about their jobs.

And, and, and, um, I just want to remind them that the purpose of your job and the tasks and the tools that you use to do your job are related not the same.

I've been doing my job for 33 years, some of the longest running tech CEO in the world of the 34 years.

And the tools that I've used to do my job has changed continuously in the last 34 years.

And sometimes quite dramatically, you know, over the course of couple to three years.

And, and the, the, the one story that I, I really want to make sure that everybody hears is the story, the, the first job that that computer signed to said AI researcher said was going to go away was radiology.

Because computer vision was going to achieve superhuman levels.

And it did, see the computer vision was superhuman in 2019, 2020, maybe maybe a little bit later, 2020.

Okay, and so it's been a long time since computer vision has been superhuman.

And so the prediction was radiologists would go away because studying radiology scans was thing at the past.

AI will do that.

Well, they were absolutely right.

Computer vision is completely superhuman.

Every radiology platform and package today is driven by AI.

And yet, the number of radiologists grew.

And so the question is why, and we now have a shortage of radiologists in the world.

And so one, the alarmist warning went too far and it's scared people from doing this profession that is so important to society.

And so it did harm.

Now why was it wrong?

The reason why is because the purpose of a radiologist, the purpose is to diagnose disease and help patients and doctors diagnose disease.

And because we're able to study scans is so much faster now.

You could study more scans.

You could diagnose better.

You could, you could um, impatient faster.

We can see people more.

The hospitals are making more money.

You have more patients in the hospital.

You need more radiologists.

I mean, the amazing thing is it's so obvious.

This was going to happen.

The number of software engineers that Nvidia is going to grow, not decline.

And the reason for that is because the purpose of a software engineer and the task of a software engineer of coding are related not the same.

I wanted my software engineers to solve problems.

I didn't care how many lines of code they wrote.

You know, but their job, their purpose of their job didn't change.

Solving problems, working as a team, diagnosing problems, evaluating the result, looking for new problems to solve innovation, connecting dots.

You know, none of that stuff is going to go away.

Do you think it's possible that that's even take coding?

You think the number programmers in the world might increase?

Yes.

And the reason for that is this, what is the definition of coding?

I believe that the definition of coding is up today.

It's simply specifying specification and maybe if you want to be rather directive, you could even give it an architecture of the software the year you wanted to write.

So the question is, how many people could do that?

Describe a specification for a computer to go, telling the computer what to go build.

How many people?

I think we just went from 30 million to probably one billion.

And so every carpenter in the future will be a coder, except a carpenter with AI is also an architect.

They just increase the value that they could deliver to the customer.

Their their artistry just elevated tremendously.

I believe that every accountant, you know, also your financial analyst, also your financial advisor.

So all of these professions have just been elevated.

And if I were carpenter, I see AI, I would just completely go berserk.

You know, the services that can bring to my clients, if I were a plumber, completely go berserk.

And the people that are currently programmers and software engineers, I think they're at the cutting edge, understanding intuitively how to communicate with the agents using natural language in order to design the best kind of software.

That's right.

So because over time they'll converge, but I think there's still value in getting, I think, learning how to program, like learning what programming languages are, the all kind of programming, what, what are good practices for programming languages, what are design principles for programming, that's for languages for large software systems.

And the reason for that, you know, I just say for the audience, I think the goal of the goal of specification, the artistry of specification, the goal and the artistry of it, it's going to depend on what problem you're trying to solve.

When I'm thinking, when I'm thinking about giving the company strategies and formulating corporate directions and things that we should do, I describe it at a level that is sufficiently specific, that people generally understand the direction, and it's actionable, they, it's so specific enough that they can take action on it.

But I under specify it on purpose, so that enable 43,000 amazing people to make it even better than I imagined.

And so when I'm working with engineers, when I'm working with people, I think about who, what problem am I trying to solve, who am I working with, and the level of specification, the level of architecture definition relates to that.

And so everybody's going to have to learn how where in the spectrum of coding they want to be, writing a specification is coding.

And so you might decide to be quite prescriptive, because there's a very specific outcome you're looking for.

You might decide that, you know, this is an area you want to be much more exploratory.

And so you might under specify an enable you to go back and forth with the AI to even push your own boundaries of creativity.

And so this artistry of where you are in the spectrum, this is the future of coding.

But just the one growing outside of coding, I think a lot of people rightfully so are worried about their jobs, have a lot of anxiety about their jobs, especially in the white color sector.

I don't think any of us know what to do with tumultuous times that I was come when automations and new technology arrives.

And I just, first of all, I think we all need to have compassion and there's possibility to feel sort of the burden of what the actual suffering feels like for individual people and families that lose their job.

And I think whatever you have transformer of technology, like that's coming with with artificial intelligence, there's going to be a lot of pain.

And I don't know what to do about that pain.

Hopefully it creates much more opportunities for those same people for the same kind of job as the tooling evolves and makes them more productive and makes them more fun.

Hopefully as it does in the programming, I've been having so much fun programming and I had to say, I've never had this much fun.

So hopefully it makes their job automates the boring parts and makes the creative parts, the ones that the human beings are responsible for, but still there's going to be a lot of pain and suffering.

So my first recommendation before, and this is now how I deal with anxiety.

In fact, we just talked about it earlier and normal anxiety about the future and normal anxiety about the pressure and normal anxiety about uncertainty.

I first break it down and then I'm going to tell myself, okay, there are some things you can do something about, there's something you can't do anything about, but for the stuff that you can do something about, let's reason about it and let's go do it.

If we were to hire a new college graduate today and I have a choice between two, one that has, that is no clue what AI is and one that is expert in using AI.

I would hire the one who's expert in using AI.

If I had a accountant, a marketing person, the one that is expert in using AI, supply chain, customer service, a salesperson, business development, a lawyer.

I would hire the one who is expert in using AI.

So I would advise that every college student, every teacher, should encourage their student to go use AI, every college student should graduate and be an expert in AI, and everybody, if you're a carpenter, if you're a nutrition, go use AI, go see what it can do to transform your current job, elevate yourself.

If I were a farmer, I would absolutely use AI.

If I were a pharmacist, I would use AI.

I want to see what it could do to elevate my job so that I could be the innovator to revolutionize this industry myself.

And so that would be the first thing that I would do.

And then I would also help them.

It is the case that the technology will dislocate and will eliminate many tasks.

And because it will automate it, if your job is the task, if your job is the task, then you're very highly going to be disrupted.

If your jobs purpose includes certain tasks, then it's vital that you go learn how to use AI to automate those tasks.

And then there's the world of spectrum in between.

And by the way, the beautiful thing about AI, the chatbot versions, is you can break down, you have anxiety, you can break down the problem by talking to it.

Like I've recently, it's really just incredible how much you can think through your life's problems and through, and I don't mean like therapy problems.

I mean like very practically, okay, I'm worried about my literally, I'm worried about my job, what are the skills, what are the steps I need to take, how do I get better at AI?

Everything you just said, you can literally ask and it's going to give you a point by a point plan if I mean it's just a great life coach.

Period.

I don't know how to use AI and the I goes, well, let me show you exactly.

It's very matter, but it's kind of incredible.

So people definitely show you.

You can't walk up to excellence.

I don't know how to use Excel.

You're done.

I mean, that's really what AI has done for me in all walks of life, is that initial friction of being a beginner, of using a thing for the first time.

I can literally ask about any single thing.

What are the first steps I need to take?

That's right.

And at that handholding that it does removing the friction of all the experiences that the world offers is, you know, like I mentioned to you offline, you mentioned, I'm going to China and Taiwan.

So where do I get?

Where do I get?

Where do I get?

How do I all those questions immediately answered?

It is beautiful.

When you go to Taiwan, just ask AI, what are your chances, favorite restaurants in Taiwan?

Yeah.

And it was actually, oh, yeah.

Is it accurate?

Yeah.

All right.

It's all over Taiwan.

Well, you're a rock star over there.

And like we also mentioned offline, maybe our pastoral cross, which would be really wonderful.

In Computex, in India, GTC Taiwan.

Do you think there are some things about human nature, about human consciousness?

That is fundamentally non-competitional.

Maybe something a chip, no matter how powerful can never replicate.

I don't know if the chip will ever get nervous.

And that's the, you know, of course, the conditions by which that causes anxiety or nervousness or whatever emotion.

I believe that AI will be able to recognize those and understand those.

I don't think my chips will feel those.

And therefore, how that anxiety, how that feeling, how that excitement, how that, you know, all of those feelings manifests in human performance.

For example, extremely amazing human performance athletic performance, you know, average or lesser than average.

That, that entire spectrum of human performance that comes out of exactly the same circumstances for different people, manifesting in different outcome.

manifesting in different performance, I don't think there's anything about anything that we're building that would suggest.

That two different computers being presented with all of exactly the same context would, would, of course, it would produce statistically different outcomes.

But it's not because of felt different.

Yeah, the subjective boy, there's something truly special about the subjective experience that we humans feel.

I can imagine to you, I was, I was pretty nervous talking to you.

Like I mentioned to you that the hope to fear the anxiety and just life, itself, the richness of life, how amazing everything is, how deeply we fall in love, how deeply a heart get broken, how afraid we are of death and how much pain we feel in our loved ones pass away, all of that, the whole thing.

I know it's very hard to think AI being able to a computation and devise being able to do that, but there's so many mysteries about this whole thing that we're yet to uncover that I am open to be surprised.

Yeah, I've been surprised a lot of the past few months and few years scaling can create some incredible miracles in the space of intelligence, has been truly marvelous to watch, so I'm open to surprise.

And it's just really important to break down what is intelligence, the word that word we use all the time.

It's not a mysterious word intelligence has a meaning, you know, and it's a system that, you know, it's something that we do that includes perception and understanding and reasoning and the ability to do plan and, you know, that loop, that loop is the fundamentally what intelligence is.

Intelligence is not one word that is exactly equal to humanity.

And that's, I think it's really important to separate the two.

We have two words for that.

I'm not, I don't over fantasize about, and I don't over romanticize about intelligence.

Intelligence is, and people I've heard me say it before, I actually think intelligence is a commodity.

I'm surrounded by intelligent people.

And I'm surrounded by intelligent people, more intelligent than I am, and each one of the spaces that they're in.

And yet, I have a role in that circle.

It's actually kind of interesting.

They're more educated than I am.

They went to better schools than I did.

They're deeper than in any, in the fields that they're in, all of them.

I have 60 of them.

They're all superhuman to me.

And somehow, I'm sitting in the middle orchestrating all 60 of them.

And so you got to ask yourself, what is it about a dishwasher that allows that dishwasher to sit in the middle of superhumans?

Does that make sense?

And so, but that's my point.

My point is intelligence is a, is a functional thing.

Humanity is not not specified functionally.

It's a much, much bigger word.

And our life experience, our tolerance for pain, our determination, those are, those are different words and intelligence.

And so, the thing that I, I want to help the audience understand, if I could give them one thing is, is intelligence is a word that we've elevated to very high form over time.

The, the word we should really elevate is humanity.

Character humanity.

All of those things compassion, generosity.

All of the things that you say just now, I believe those are superhuman powers.

And that now intelligence is going to be commoditized because we've spoken about it, the most important thing is your education.

The most, now even even when they said the most important thing is your education, when you went to school, there's more than just knowledge that you gained.

And so, but unfortunately, our society had put everything into one single word.

And life is more than one word.

And I'm just telling you my life, which suggests, that being lower on the intelligence curve than everybody around me doesn't change the fact I'm the most successful.

And so, and I think, I think that that kind of is, I'm trying to hopefully to inspire everybody else, that don't let the democratization of intelligence, this commoditization of intelligence, you know, cause you anxiety, you should be inspired by that.

Yeah, I think AI will help us celebrate humans more.

And I'm certainly humanity and human and I think what makes this world incredible is humans forever will be so.

And just AI is this incredible tool.

That makes us, that's exactly human is more powerful.

That's exactly right.

So much of the success of India.

And the lives of millions of people that I mentioned, depend on you.

But you're just one human, like we mentioned, mortal, like all of us.

Do you think about your mortality?

Are you afraid of death?

I really don't want to die.

I have a great life, I have a great family.

I've really important work.

This is, this is not a once in a, once in a lifetime experience suggests that it has been experienced by many people, just not one person.

This is a once in a humanity experience, what I'm going through.

And video is one of the most consequential technology companies in history.

We're doing very important work.

I take it very seriously.

And so some of the, some of the things that that of course are, are practical things.

Like how do we think about success from planning?

And I'm famous in saying that I don't believe in succession planning.

And the reason for that, the reason for that isn't because I'm immortal.

The reason for that is because if you're worried about succession planning, if you're worried, all that anxiety of succession planning, then what should you do about it?

Then you break it all way back down.

The most important thing you should do today, if you care about the future of your company, post you, is to pass on knowledge, information, insight, skills, experience, as often and continuously as you can, which is the reason why continuously reason about everything in front of my team.

Every single meeting is about a reasoning meeting.

Every moment I spend inside a company, outside a company, is about passing on knowledge to people as fast as I can.

Nothing I learned ever since sits on my desk longer than, you know, a fraction of a second.

I'm passing that information that, oh my gosh, this is cool.

Before I even finished learning all of it myself, I've already pointing it to somebody else.

Get on this.

This is so cool.

You're going to want to learn this.

And so I'm constantly passing knowledge and powering people, elevating the capability of everybody around me.

So that, the outcome that I seek, that I hope for, is that I die on the job.

And hopefully, I die on the job instantaneously.

And there's no long fears of suffering, you know?

It's a well from a fan perspective.

Given your extremely an enormous positive impact on civilization, of course, I hope you keep going.

But also it's just fun to watch.

What he is doing here, you know, it's just the rate of innovation.

And I'm a huge fan of engineering.

It's so much incredible engineering is continuously being done by Nvidia.

It's just fun to watch.

It's a celebration of humanity.

It's celebration of great builders.

It's a celebration of great engineering.

So it represents something special.

So I hope you and Nvidia keep going.

What gives you hope about this whole thing we've got going on?

About humanity, about the future of humanity.

When you look out, and you think about the future quite a bit, when you look out 10, 20, 50, 100 years from now, what gives you hope?

I've always had, I've always had great confidence in the kindness, the generosity, the compassion, the human capacity.

I've always been extremely confident of that.

Sometimes more so than I should.

And I get taken advantage of, but it doesn't, it doesn't ever cause me not to.

I start with always that that people want to do good.

People want to help others.

And vastly I am proven right, constantly proven right.

And often I exceed my expectations.

And so I have complete confidence in the human capacity.

I think the thing that the things that give me incredible hope is what I see as I extrapolate, as I what I see now is possible.

And as I extrapolate, based on the things that we're doing, what will very likely happen.

And that there's so many things that we want to solve.

There's so many problems we want to solve.

There's so many things that we want to build.

There's so many good things that we want to do that are now within our reach.

And within the reach of my lifetime, you just can't possibly not be romantic about that.

You know what I'm saying?

Yeah, what an exciting time to be alive.

Yeah, like truly, truly so.

How can you not be romantic about about that?

The fact that there is a reasonable thing to expect at the end of the disease.

It's a reasonable thing to expect.

It's a reasonable thing to expect.

It's a reasonable thing to expect.

That pollution will be drastically reduced.

It's a reasonable thing to expect that traveling at the speed of light is actually in our future.

And then, you know, not for a long distances, but short distances.

You know, people ask me how, and it was first of all, very soon, I want to put a humanoid on a spaceship.

And it's going to be, you know, my humanoid.

And I want to send it out as soon as possible.

And it's going to keep improving and enhancing along the flight.

And then when it's time, all of the all of my consciousness has already been, you know, so much of my life has been uploaded in the internet, take all my inbox, take everything that I've done, everything I've said, you know, it's been collect and becoming my AI.

And I'm just, you know, when the time comes, you know, we'll just send that at the speed light, catch up with my robot.

I was brilliant.

I mean, but for me, that's sort of application focused.

But I also believe a curiosity, uh, maxing perspective.

I just, all of those mysteries is so much fascinating scientific questions there.

Understanding the biological machine is a, it's right around the corner.

It's, it's not 10 years.

It's five years probably.

And then your biological machine, the human mind and cracking physics, theoretical physics open, it's so exciting.

Explaining consciousness, that one would be awesome.

And it's all within our region.

Justin, thank you so much for everything you've done over the years.

Thank you for everything you're doing for the world.

Thank you for being who you are.

I can tell you're a great human being.

And I wish you incredible success this year.

I can't wait.

As a fan, I can't wait to see what you do next.

And hopefully I'll see you in Taiwan.

And thank you so much for talking today.

Thank you, Lex.

I had a great time.

And, and also, if I could just say one more thing.

Yes.

And thank you for all the interviews that you do, the depth, the, the respect that you go through with and the research that you do to reveal, you know, for all of us, the amazing people that you've interviewed over the years.

I've enjoyed, I've enjoyed them immensely.

And, and as an innovator to have created this long form unbelievable.

And, and yet, you know, it's just captivating.

So anyways, thank you for everything.

It means the world.

Thank you, Jess.

Thank you, Lex.

Thank you for listening to this conversation with Jensen Huang.

To support this podcast, please check out our sponsor in the description where you can also find links to contact me, ask questions, give feedback, and so on.

And now, let me leave you with the words from Alan K.

The best way to predict the future is to invent it.

Thank you for listening and I hope to see you next time.