Jensen Huang LIVE: Nvidia's Future, Physical AI, Rise of the Agent, Inference Explosion, AI PR Crisis

President Trump Jesus and

“And I'll let you pick which order we do that”

But what an amazing run you've had an an a great event

Every industry is here every tech companies here every AI company is here incredible I'm doing all of you if you were building a global financial system who first principles today You wouldn't build it on 50 year old legacy rounds you'd build airwallics one AI made of platform for global accounts cards and Amons it's designed to make the entire world feel like a local market others are bolting AI on to broken infrastructure But airwallics was built for the intelligent era from day one stop paying legacy tax and start building the future at

Airwallics dot com slash all in airwallics build the future and One of the great announcements of the past year has been rock when you made the purchase of rock Did you realize how insufferable chaf chamoth would become

“We have to deal with them every week. I know it. You had to deal with them for the six-week clothes”

I know it's like two weeks two weeks. It's all coming back to me now Making me rather uncomfortable. The thing is Many of our strategies are are presented in In broad daylight at GTC years in advance of when we do it two and a half years ago I introduced the operating system of the AI factory and it's called dynamo dynamo as you know is

And peace of instrument a machine that was created by Seemants to turn essentially water into electricity and Dynamo I will power the factory of the last industrial revolution so I thought it was the perfect name for the operating system of the next Industrial revolution the factory of that And so inside dynamo the fundamental technology is disaggregated in front

Jason, I know you're you're super technical. Absolutely. I know it. I'll let you take this one go ahead and Yeah, thank you. I knew you wanted to jump in there for a second But it's it's this aggregating inference which means the the pipeline the processing pipeline of inference is Extremely complicated and in fact it is the most complicated computing problem today

Incredible scale lots of mathematics of different shapes and sizes and we came to came up with the idea that you would change

You would you would disaggregate parts of the processing such that some of it can run on some GPUs Rest of it can run on different GPUs and that led to us realizing that maybe Even this aggregated computing could make sense that we could have different heterogeneous nature of computing that same sensibility let us to melanize Yep, you know today and video computing is spread across

GPU CPUs switches scale-up switches scale-out switches networking processors and now we're gonna add Grok to that and we're gonna put the right workload on the right chips You know, we just really evolved from a GPU company to an AI factory company

“I mean, I think that was probably the biggest takeaway that I had you're seeing this fundamental disaggregation where we've gone from a GPU and now”

You have this complexion of all these different options that will eventually exist the thing that you guys said on stage Your use that on stage was I I would like the high value inference people to take a listen to this and 25% of your data center space You said should be allocated to this grock LPU We should you come rock to about 25% of the Vera Rubens energy in the data center So can you tell us about how the industry looks at this idea of now

Basically creating this next generation form of disaggregated pre-filled decode disag and how people you think will react to it

Yeah, and take a step back and at the time that we added this we went from large language model processing To agentic processing Now when you're running an agent you're Accessing working memory your accessing long-term memory you're using tools You're really beating up on storage really hard

You have agents working with other agents some of the agents are very large models some of them are smaller models Some of them are diffusion models some of them were auto or auto regressive models and so they're all kinds of different types of models inside this data center We create a Vera Ruben to be able to run this extraordinarily diverse workload my senses And so we added we used to be a one-rack company We now add a four more racks right so Nvidia's Tam if you will increased from what it whatever it was to probably something call it

Some of it will be a lot of it. I'm hoping will be Grock processors and some of it will be CPUs and they're all gonna and a lot of it's gonna be networking processors And so all of this is gonna be running basically the computer of the AI revolution called agents Right the operating system of what modern modern industry what about embedded applications? So you know my daughter is teddy bear at home wants to talk to her

What goes in there is it a custom asik or does their end up becoming much more kind of a broader set of Tam With developing tools that are maybe different for different use cases of the edge and in an embedded application We think that there's three computers in the problem at the largest at the largest scale when you stick take a step back There's one computer that's really about training the AI model developing creating the AI another computer for Evaluating it depending on the type of problem you're having like for example

“You look around there's all kinds of robots and cars and things like that. You have to evaluate”

these robots inside a virtual gym That represents the physical world so it has to be software that obeys the laws of physics and That's a second computer. We call that omniverse the third computer is the computer at the edge the robotics computer That robotic computer one of them could be self driving car. Another one's a robot another one could be a teddy bear little tiny one for a teddy bear

One of the most important ones is one that we're working on that basically turns the telecommunications base stations

Into part of the AI infrastructure. So now all of the it's a two trillion dollar industry All of that in time will be transformed into an extension of the AI infrastructure and so radios radios will become Edge devices Factories warehouses you name it and so so there are three these three basic computers All of them, you know are gonna be necessary. Jensen last

“Last year, I think you were ahead of the the rest of the world and and say an inference isn't gonna”

I thought it was just last year. Yes. Is it what is it gonna one million X is gonna one billion X? Yeah, right. I think people at the time thought it was pretty hyperbolic because the world was still focused on pre-scalene on training Here we are now inferences exploded. We're inference constrained You announced an inference factory that I think is leading edge that's gonna be 10X better in terms of throughput to the next factory

But yet if you if I listen to what the chatter is out there. It's that your inference factory is gonna cost 40 or 50 billion

And the alternatives the custom a6 AMD others are gonna cost 25 to 30 billion and you're gonna lose share So what did you talk to us? What are you seeing how do you think about share and does it make sense for all these folks to pay something That's a two X premium to what others are marketing the big take away the big idea is that You should not equate the price of the factory and the price of the tokens the cost of the tokens

It is very likely that the fifty billion dollar factory and in fact I can prove it that the fifty billion dollar factory We'll generate for you the lowest cost tokens and the reason for that is because we produce these tokens at Extraordinary efficiency

10 times you know the difference between 50 billion now it turns out 20 billion is just land power and shell right right and then on top of that

You have storage anyways networking anyways you got CPUs anyways you got servers anyways You got cooling anyways the difference between that GPU being one X price or half X price Is not between 50 billion and 30 billion pick your favorite number But let's say between 50 billion and 40 billion that is not a large percentage when the 50 billion dollar Data center is actually ten times the throughput right

That's the reason why I said that even for most chips If you can't keep up with the state of the technology and the pace that we're running even when the chips are free is not cheap enough Yeah, can I just ask a general strategy question? Yeah, I mean you're running the most valuable company in the world this thing is gonna do 350 plus billion of revenue next year 200 billion of free cash flow. It's compounding at these crazy rates

“How do you decide what to do like how do you actually get the information?”

I mean it's famous now these sort of emails that are people are meant to send you but how do you really decide? To get an intuition of how to shape the market where to really double down where to maybe pull back Where to actually go into a green field? How does that information get to you? How do you decide pieces in a final analysis that's the job of the CEO? Yeah, and our job is to

Amazing technologists great people all over the company, but we have to shape that future

Well part of it has to do with is this something that's insanely hard to do if it's not hard to do We should back away from it and the reason for that if if it's easy to do obviously

“Lots of competitive a lot of competitors. Yeah, is this something that has never been done before?”

That's insanely hard to do and that somehow taps into the special superpowers of our company And so I have to find this confluence of things to that meets the standard and In the end we also know that a lot of pain and suffering is gonna go into it Yeah, there are no great things that are invented because it was just easy to do and just like first try here We are and so if it's super hard to do nobody's ever done it before

It's very likely that you're gonna have a lot of pain and suffering. Can you and so you better enjoy it? So can you can you just look at maybe three or four the more long-tailed things you announced and just talk about the long-term Biability of whether it's the data centers in space or whether it's what you're trying to do with A-dass and autos or You know what you're trying to do on the biology side just give us a sense of like how you see some of these curves Inflecting upwards in some of these long-tailed business. Excellent

Physical AI large category We believe and I just mentioned we have three computing systems all the software platforms on top of it physical AI as a large category It's technology industries first opportunity To address a 50 trillion dollar industry that has largely been You know void of technology until now and so we need to invent all of the technology necessary to do that

I felt that that was a 10-year journey. We started 10 years ago. We're seeing and inflecting now

It is a multi-billion dollar business for us as close to ten billion dollars a year now and so it's a big business

“And it's growing exponentially and so that's number one. I think in the case of digital biology”

I think we are literally near the chat GPT moment of digital biology We're about to understand how to represent genes Proteins cells. We all need to understand chemicals and so the ability for us to represent and understand the dynamics of Building blocks of biology that's a couple of two three five years from now in five years time I completely believe that the healthcare industry or digital biology is going to inflect and so these are a couple of the really great ones

And you could see they're all around us agriculture agriculture collecting now no question. Yeah I want to take you from the data center to the desktop The company was built in large part on hobbyists Video gamers and and all those graphic cards in the beginning and you mentioned in front of I think 10,000 people here Just clawed

Open claw clawed code and what a revolution agents have become and specifically the hobbyists who are really where a lot of energy We see you know a lot of the innovation breaks one desktop's you announced one here I believe it's the Dell 6800

This is a very powerful workstation to run local models 750 gigs of RAM Obviously the the Mac studio sold out everywhere in my company We're moving to open claw everything free bird just got claw peeled you got claw peeled I understand in your obsessed with these

“What is this from the streets movement of creating open source?”

Agents and using open source on the desktop mean to you. So great. Where is that going? Yeah, so great. First of all, let's take a step back In the last two years, we saw basically three inflection points the first one was generative chat GPT Brought AI to the common Everybody to our awareness, but the fact that matters the technology set and plain sight months before GPT

It wasn't until chat GPT put a user interface around it made it easy for us to use that generative AI took off Now generative AI as you know Generates tokens for internal consumption as well as external consumption yeah internal consumption is thinking which let to reasoning O1 and O3 Continue that wave of chat GPT grounded information made AI not only answer questions, but answer questions in a more grounded way useful

We started seeing the revenues and the the economic model of open AI start to inflict Then the third one was only inside the industry that we saw claw code the first Agentic system that was very useful really revolutionary stuff But a but co-code was only available for enterprises most people outside

Never saw anything about cloud code until open claw open claw

Basically put into the popular consciousness What an AI agent can do?

open but it Formulates its structures a

“type of computing model that is basically reinventing computer and all together it has a memory system”

Scratch is a short-term memory file system. It has scales it has scales

Did you say skills or scales skills? Oh skills. You have scales. Yeah, theoretically. Yeah, so the first thing first thing

It it you know it has resources and manages resources. It's it does scheduling Yep, right and it cron jobs. It could it could spawn off agents it could you know it could decompose a task and and Cause and solve problems does scheduling it has I/O subsystems again. You know input has output it connect to what's app and it also It has a API that allows it to run multiple types of applications called skills. Yeah, these four elements Fundamentally define a computer. Yeah, and therefore what do we have? We have a personal

Artificial intelligence Computer for the very first time open source. It's open source. It runs literally everywhere and so this is now The this is the op this is basically the blueprint the operating system of modern computing Yeah, and it's gonna run literally everywhere now of course one of the things that we have to help it do is Whenever you have a gentic software

“You have to make sure that an agentic software has access a sensitive information and that execute code it could communicate externally”

We have to make sure that all of it has to be governed all of it has to be secure and that we have policies that it that gives These agents two of the three things, but not all three things at the same time and so the governance part of it We contributed to Peter Peter Steinberger was here and and so we've got a amount of great engineers working with him to help secure and Keep that thing so that it could protect our privacy protect our security. Jensen that paradigm shift Makes some of the AI legislation that has passed around the country to regulate AI and a lot of the proposed legislation

Effectively moot doesn't it can you just comment for a second on how quickly the paradigm shift kind of obviates a lot of the Models for regulatory oversight of AI, which is becoming a very hot topic in politics right now Well, this is this is the part that the

We're just with policymakers we need to we need to always get in front of them and Brad you do a great job doing this. We had given front of them and informed them about the state of the technology

What it is what it is not it is not a biological being it is not alien it is not conscious It is computer software Yeah, and and it is not something that we say things like we don't understand it at all

“It is not true. We don't understand it all we understand a lot of things about this technology and and so so I think one”

We have to make sure that we continue to inform the policy makers and not affect not allowed Dumerism and extremism to affect how Policy makers think and understand about this technology However, however, we still have to recognize the technology is moving really fast and don't get policy ahead of the technology too quickly and The risk that we we run as a nation

Our greatest source of national security concern with respect to AI is that other countries adopt this technology While we are so Angry at it or afraid of it or somehow paranoid of it that our industries our society don't take advantage of AI So I'm just mostly worried about the diffusion of AI here in the United States. Can you just double click if you are in the seat In the boardroom of anthropic over that whole scuttle, but with the Department of War

It sort of builds on this idea of People didn't know what to think. It's sort of added to this layer of either resentment or fear or just general mistrust that people have sometimes at the software levels of AI What would you would do you think you would have told Dario and that team to do maybe differently to try to

Change some of this outcome in some of this perception the first thing that I would say among anthropic is first of all the technologies incredible

We are a large consumer of anthropic technology. Yeah, really admire their focus on security really at Myers. They're focused on safety The the culture by which they went about it the the technology excellence by which they went about it really fantastic I would say that that The the desire to warn people about the capability the technology is is also Really terrific. We just have to make sure that we understand that the world has a spectrum and that that

Warning is good scaring is less good All right, and because this technology is too important to us right and and I think that it is fine to

We can't completely predict the future and the ability and to say things that that are quite extreme

quite catastrophic that there's no evidence of it happening Could be more damaging than people think and and of course we are technology leaders There was there was a time when nobody listened to us. Yeah, but now because technology is so important in the social fabric

“Such an important industry so important to national security our words do matter and I think we have to be much more”

Circumstance, we have to be more moderate. We have to be more balanced. We have to be more for more thoughtful. Well, I You know, I would nominate you. I think the industry's got to get together 17% popularity of AI in the United States. I mean, we see what happened in nuclear Right. We basically shut down the entire nuclear industry and now we have a hundred vision reactors being built in China and zero on the United States We hear about moratoriums on data centers, so I think we have to be a lot more proactive about that

But I want to go back to this agentic explosion that you're seeing inside your company the efficiencies the productivity gains inside your company There's a lot of debate whether or not we're seeing ROI Right, and you and I entering into into this year the big question was are the revenues going to show up

Are the revenues going to scale like intelligence and then we had this kind of oppenheimer moment of five six billion dollar month by anthropic and February

Do you think as you look ahead you announced a trillion dollar? You know visibility into a trillion dollars have just black well and Vera Rubin over the course of the next couple years When you see this happening at anthropic and open AI Do you think we're on that curve now where we're going to see revenues scale in the way that intelligence is scaling when you look around When you I'll answer than there's a couple of different ways when you look around this audience

You will see that anthropic and open AI is represented here But in fact, every but ninety nine percent of everything that is here is all AI and it's not anthropic and open AI Right, and the reason for that is because AI is very diverse

I would say that the second most popular model as a category is open models

Number one is yeah open open source open weights open source Open AI is number one open sources number two very distant third is anthropic and that tells you something about the scale of all of the AI companies that are here And so so it's important to recognize Recognize that let me let me come back and say a couple things one when we went from generative to reasoning the amount of

Computation we needed was about a hundred times right when we went from reasoning to a Gentic the Computation is probably another hundred times now we're looking at in just two years Computation went up by fact 10,000 x meanwhile

People pay for information, but people mostly pay for work, yes Talking to a chatbot and getting an answer is super great right helping me do some research unbelievable But getting work done I'll pay for and so that's where we are a Gentic systems get work done They're helping our software engineers get work done and and so then you take that you got 10,000 x more compute You get probably at this point a hundred x more consumption now. Yes, yeah, and we haven't even started scaling yet

“We are absolutely at a million x which is a I think a great place to talk about the number of”

10 trillion mark in half but when he 30,000 at the company something we have 43,000 employees You know, I would say 38,000 our engineers the Conversation we've had on the pod a number of times is oh my god look at the token usage in our companies It is growing massively and Some people are asking hey when I join a company how many tokens do I get because I want to be an effective employee and

You postulated I believe during your two and a half hour keynote Pretty long keynote well done That you were spending was well done. It would be shorter. Yeah, it has time to do So you guys so you guys know so you guys know there is no practice and so it's a grip and and rip and rip Yeah, so I just want to let you know I was writing the speech while I was giving the speech

You guys back here was a math 75,000 in tokens for each engineer something like that

“So are you spending in Nvidia a billion two billion dollars on tokens for your engineering team right now?”

We're trying to let me give you the thought experiment. Let's say you have a soft wengineer or AI researcher And you paid them five hundred thousand dollars a year. We do that all the time Okay, this is happening all of the time That five hundred thousand dollar engineer at the end of the year

$5,000 I will go ape something else. Yes, right if that if that five hundred thousand dollar engineer did not consume At least two hundred fifty thousand dollars with a tokens. I am going to be deeply Alarmed

“Okay, and this is no different than one of our chip designers who says guess what?”

I'm just gonna use paper and pencil I don't think I'm gonna need a cat tool This is a real time shift. Yeah, thinking about these all star employees

It almost reminds me of what we learned in the MBA when LeBron James started spending a million dollars a year

Just on his health of his body like in maintaining it. That's right. Here he is at age 41 still playing It really is hey if these are incredible knowledge workers why wouldn't we give them Superhuman abilities that's exactly where does that go if we if we extrapolate out two or three years from now? What is the efficiency of that all star at an Nvidia and what they're able to accomplish? They're just me look like well first of all things that that that

Wow, this is too hard That thought is gone This is gonna take a long time that thought is gone We're gonna need a lot of people that thought is gone

This is no different than in this in the last industrial revolution somebody goes

Boy, that building really looks heavy Nobody says that right nobody wow that mountain looks too big nobody says that right everything that's too big too heavy Takes too long those thought those ideas are all good reduced to creativity. That's right

“Can you come up exactly right which means now the question is how do you how do you work with these agents?”

Well, it's just a new way of doing computer programming and the future in the past we code and the future We're gonna we're gonna write ideas architectures specifications We're gonna organize teams we're gonna give you we're gonna help them define how to evaluate the definition of good versus bad what's the what does it look like when something is of great outcome how to iterate with you? How to brainstorm that's really what you're looking for and I'm I think that every engineer is going to have

100 a hundred agents back to the PR problem the industry has right now You have executives like David Freyberg with all hollow Who's looking at literally taking through the use of technology your technology and AI the number of calories produced And making high quality calories What is the factor you think you can bring the cost down freeberg?

What impact is this vision have what you're doing zero shot genomic modeling and it works

“Yeah, and you have that moment and you're like holy”

Honestly like and and that's after People are replacing entire enterprise software stacks in a night. I did something in 90 minutes I was telling the guys about Replace the whole software stack and like a whole bunch of workload 90 minutes on clawed ran in the same Genetic system built the whole thing deployed it and we got we were on a Sunday night 10 p.m. I was done at 11 30

I went to bed as the CEO you replaced yeah and everyone on my management team had to do a similar exercise over the weekend what we saw on Monday. I was like It's over but the technical stuff the science stuff We did something in 30 minutes using auto research and I love your view on auto research and what that tells us About how far we still have to go in terms of efficiency

But using auto research and a chunk of data Something was published internally that we said oh my god and that would normally be a PhD thesis that would take seven years It would be one of the most celebrated PhD species we've ever seen in this field and it would be in the journal science And it was done in 30 minutes on a desktop computer running on auto research with all the data We just ingest we got it on Friday and we're like hey, let's try it try booted up going to get help downloaded auto research and ran it

And you see everyone's face just go like And then the potential of what this is unlocking for us is like the kind of thing that would take seven years and it happened in 30 minutes And we're experiencing it in genomics and we're like this is unbelievable So I think like the acceleration Is widening the aperture for everyone in a way that like you didn't imagine a few years ago

But just going back to the auto research point. Can you just comment on what you think about the fact that this thing got published With 600 lines of code in a weekend and the capacity that it has to run locally and achieve what it can achieve with all of these diverse data sets And what that tells us about the early stages we are in terms of optimization on algorithms and hardware

The fundamental reason why open claw is so incredible number one is it's come it's confluence

It's timing with the breakthroughs in large language model. Yeah, it's timing was perfect. It was impeccable Now in a lot of ways Peter wouldn't have come up with it probably if not for the fact that clawed and GPT and Chad GPT have reached a level that is really very good, right? It is also a New capability that allows these models to tool use The tools that we've created over time

omniverse and blender and auto-desk and all of these tools are going to continue to be used. There's some some people say that that the enterprise IT software industry is going to get destroyed There's it's there's a let me give you the alternative view The enterprise software industry is limited by buts and seats It's about to get a hundred times more agents banging on those tools

They're going to be agents banging on sequel. They're going to be agents banging on vector databases Agents banging on blender agents banging on Photoshop and the reason for that is because those tools are First of all, do a very good job. Second, those tools are the conduit between us In the final analysis of the work is done. It has to be represented back to me in a way that I can control right and I know how to control those tools And so I need everything to be put back into synopsis. I want everything to put back into cadence

“Because that's how I control it. That's how I've ground truth. Let me ask you a question about open source”

So we have these closed source models. They're excellent. We have these open-weight models many of the Chinese models are incredible

Absolutely incredible Two days ago. You may not have seen this because you were busy on stage, but There was a training run that happened in this crypto project called bit tensor subnet three They managed to train a four billion parameter Lama model totally distributed with a bunch of people Contributing

Access compute, but they were able to do it statefully and manage a training run Which I thought was like a pretty crazy technical accomplishment. Yeah, because it's like random people and yeah person gets to live here Our modern version of folding on home. Exactly. Yeah, so what what do you think about the end state of open source? Do you see this Decentralization of architecture as well and decentralization of compute to support

“Open weights and a totally open source approach to making sure AI is broadly available. I believe we fundamentally need”

Models as a first class product proprietary product as well as models as open source

These two things are not A or B. It's A and B. There's no question about it and the reason for that is because Models is a technology not a product models a technology not a service for the vast majority of consumers The horizontal layer or the general intelligence. I would really really love not to go find two my own right I would really love to keep using chat GPT. I'd love to use cloud. I love to use Gemini. I love to you at use X And they all have their own personalities as you know

Which just kind of depends on my mood and depends on what problem I'm trying to solve you know I might you know do it on an X or I might do it on on chat GPT and so that that segment of the the industry is thriving It's gonna be great. However There are all these industries Their domain expertise their specialization has to be channeled has to be captured in a way that they can control

And that it can only come from open models the open model industry work contributing tremendously too. It is near the frontier And quite frankly even if it reaches the frontier

“I think that products as a service world class products as a as a model's as a product is gonna continue to thrive every start up”

We're investing in now is open source first and then going to the proprietary model

Yeah, and the beautiful thing is because you have a great router you connect a two by on on first day every single day You're gonna have access to the world's best model and and then to give you time to cost reduce and fine tune and specializing So you're gonna have world-class capabilities out the shoot every single time. Let that's of course Nobody wants the US to win the global AI race more than you right but oh a year ago The Biden era diffusion rule really was an anti American diffusion of AI around the world

So here we are a year into the new administration Give us a grade where is where are we in terms of global diffusion and the rate at which we're spreading US AI technology around the world are we an A or we a B or we a C what's working what's not working Well first of all President Trump wants American industry to lead he wants American technology industry to lead

He wants American technology industry to win he wants us to spread American technology around the world He wants United States to be the wealthiest country in the world. He wants all of that at the current moment as we speak Nvidia gave up a 95% market share in the second largest market in the world and we're at zero percent President Trump that's right President Trump wants us to get back in there and and the first thing is

We've got many companies who have requested for licenses. We've applied for licenses for them and we've got approved licenses from sick secretary

How a lot Nick now we've we've informed the Chinese companies and many of them have given us purchase orders And so we're gonna we're in the process of cranking up our supply chain again to go ship I think at the highest level Brad

“I think one of the things that we should acknowledge is this our national security”

is diminished when we don't have Access to miniature Motors rare earth minerals. It's diminished when we don't control our telecommunications networks It's diminished when we can't provide for sustainable energy for our country

It is fundamentally diminished every single one of these industries is an example of what I don't want the AI industry to be right when you we look forward in time and we say what do we want what is that what does it look like When American technology industry American AI industry leads the world We can all acknowledge that there is no way that AI models is one universally It is we can all acknowledge that is an outcome that makes no sense. However we can all

Imagine that the American textack from Chits to computing systems that the platforms are used broadly by the world where they build their own AI They use public AI they use private AI whatever and they can build their applications in their society I would love that the American textack is 90% of the world. Yes. I would love that The alternative if it looks like solar rare earth

Magnets motors telecommunications I consider that a very bad outcome for national security Yeah, how much are you monitoring the situation With the conflicts around the world right now and how much is it where are you Jensen so China and Taiwan and then helium Availability coming out of the Middle East I understand can be a supply chain risk to semiconductor manufacturing

“How much do these situations worry you how much are you spending on them?”

Well, first of all, I think in Middle East I have we have 6,000 families there. Yeah, we have a lot of

Iranians at Nvidia and their families are still in Iran and so so we have we have a lot of families there The first thing is is they're quite anxious they're quite concerned quite scared We're thinking about them all the time Where monitor and keeping an eye on them all the time they have a hundred percent of our support I've been asked several times are we still considering

Being in Israel we are one hundred percent in Israel we are a hundred percent behind the families there We are a hundred percent in the Middle East. I was also asked, you know, given what's happening in the Middle East Is that an area where we believe that we can expand artificial intelligence too? I believe that there's a reason we went to war and I believe at the end of the war Middle East will be more stable than before and so if we were there if we were considering it before

We should absolutely be considering it after and so I'm a hundred percent in on that with respect to which with respect to the Taiwan We have to do three things one We have to make sure that we re-industrialize the United States as fast as we can And whether it's the chip manufacturing plants the computer manufacturing plants or the AI factory Doing on that we're doing excellent

With by by gaining the strategic support by gaining the friendship of the supply chain of Taiwan By gaining their friendship by gaining their support. We were able to

Build Arizona in Texas, California at incredible rates. They're they are genuinely a strategic partner

We we really they deserve our support. They deserve our friendship They deserve our generosity and they're doing everything they can to accelerate the manufacturing process for us

“And so so I think that's number one number two. We ought to diversify the manufacturing supply chain and whether it's South Korea”

Whether it's it's Japan it's Europe. We ought to we ought to diversify the supply chain make it more resilient in number three Let's be let's let's demonstrate restraint and While we're reducing increasing our diversity and resilience. Let's not Press push I need to be patient. It's healing of a problem a lot of reports. You know, I think helium could be a problem

But it's also the case that the supply chain probably has a lot of buffer in it. Yeah, these kind of things tend to have a lot of buffer But but you know You've made massive progress in self-driving made a big announcement. You've added many more partners including

With Uber that you're going to have a number of cars on the road for many different manufacturers. Your bet is I believe that there is going to be an Android Type open source platform that you're going to play a major part in with dozens of car providers and then maybe on the other side

“There could be an iOS with Tesla or Waymo. What's your strategy?”

Thinking there and how that chessboard emerges because it feels like You have a pretty deep stack and in some ways you're competing and in other places you're collaborative. Yeah It's taking a step back. We believe that everything that moves will be autonomous Completely or partly some day number one number two. We don't want to build self-driving cars But we want to enable every car company in the world to build self-driving cars

And so we build all three computers the training computer the simulation computer the valuation evaluation computer as well as the car computer We develop the world's safest driving Operating system. We also created the world's first Reasoning autonomous vehicle so that it could decompose complicated scenarios into simpler scenarios that it knows how to navigate through

Just like us reasoning systems and so that reasoning system called alpamyo has enabled us to achieve incredible results

We Open this we vertical optimization. We horizontally innovate and we let everybody decide do you want to buy one computer from us in the case of Elon and Tesla they buy our training computers Do they want to buy our training computer and our simulation computers or do you want to let us Work with us to do all three and we can put the car computer in your car so we you know our attitude is we want to solve the problem

We're not the solution provider

“And work delight however you work with us. Let me bet on this question because I think it's like it's so fascinating”

You actually do create this platform a thousand flowers are blooming But it's also true that some of those flowers want to now go back down in the stack and try to compete with you a little bit Google has TPU Amazon has Inferentia and training him you know everybody sort of spinning up their own version of I think I can out in video and video Even though they also tend to be huge customers. How do you navigate that and yeah, what do you think happens over time and

Where do those things play in the complexion of this kind of they are really great you know first of all We're the only AI company where an AI company we build foundation models we're at the frontier many different domains We build every single every single layer every single stack We're the only AI company in the world that works with every AI company in the world

They never show me what they're building and I always show them exactly what I'm building right yeah

And so so the confidence comes from this one We are delighted to compete on what is the best technology and to the extent that to the extent that we can continue to run fast

“I believe that buying from Nvidia still is one of the most economic things they could do and I just”

Incredible confidence there number one number two Where the only architecture that could be in every cloud and that gives us some fundamental advantages where the only architecture You could take from a cloud and put into on-prem in the car in any region space. That's right in space and so there's a whole whole part of our market about 40% of our of our business most people don't realize this 40% of our business Unless you have the kudos stack unless you can build an entire AI factory you have the customers

Don't know what to do with you. They're not trying to build chips They're not trying to buy chips. They're trying to build AI infrastructure and so they want you to come in with a full stack and we've got the whole stack And so surprisingly Nvidia's gaining market share If you look at what we are today, we're gaining share. Do you think what happens is these guys try and they realize oh my god It's too much and then they come back is that why this share grows well well gaining share for several reasons one

Our velocity has gone We help people realize it's not about building the chip. It's about building the system and that system is really hard to build And so they're they're business with us is increasing in the case of AWS I think they just announced I think was yesterday that they're going to buy a

A million chips in the next couple years. I mean

That's a lot of chips from from AWS and that's on top of all the chips. They've already bought and so we're delighted to do that Number one, we're gaining share this last couple of years because we now have anthropic coming to Nvidia meta SL is coming to Nvidia and The growth of open models is incredible and that's all on video and so we're growing in share because of the number of models

All of these companies are outside the cloud and they're growing regionally in enterprise and industries at the edge

“And that entire segment of growth is you know really hard to do if it's just building an asset. Brad”

Related to that And not to get in the weeds on the numbers, but analysts don't seem to believe

Right, so if you look at the consensus forecast you said compute could one million x

Right, and yet they have you growing next year at 30% the year after that at 20% and in 2029 which is supposed to be a monster year at seven percent Right, so if you just if you take your Tam and you apply their growth numbers It suggests that your share will plummet. Do you see anything in your future order book that would make that correct Yeah, first of all they just don't understand the scale and the breadth of AI yeah

Yeah, I think that's most people think that AI is in the top five hyperscalers right that's right There's also north of doxie around these law of large numbers where you know they have to go back to their investment banking risk committee and show some model They're not gonna believe in their minds that it's five trillion goes to 15 trillion There's a lot of the doubt it can go to seven or they need a lot of company

“It's all just see why is happening before so you can say well and and because because that you have to redefine what is that you do”

There was somebody who made an observation recently that Nvidia Jensen how can you be larger than Intel in servers

And the reason for that is because the CPU market of the entire data center was about 25 billion dollars a year

We do 25 billion dollars a year as you guys know in a very and the time that we were sitting here And so obviously Obviously That was a joke, yes, but it's all in podcast really good. Oh, everything on the show is right Not guidance but anyhow any how it the point is how big you can be

Yeah, depends on what is it that you make right and video's not making chips Number one making chips does not help you solve the AI infrastructure problem anymore. It's too complicated Number three most people think that AI is narrowly in the things that they talk about in the here and see It's AI's much

Open AI's incredible they're gonna be enormous andthropics incredible. They're gonna be enormous

But AI is going to be much much bigger than that. Yeah, and we addressed that segment. Tell us about data centers in space for a second. Yeah We're already in space. How should the layman Think about what that business is versus when you hear about these big data center build-outs that's happening in on the ground Well, we should definitely work on the ground first because we're already here and Number one number two. We should prepare to be out in space and obviously there's a lot of energy in space

The challenge of course is that cooling You can't take advantage of conduction or actions that and so you can only use radiation and radiation requires very large Services and so now that's not an impossible thing to solve and there's a lot of space in space But nonetheless The expense is still quite there is is there. We're gonna go explore it. We're already there. We're already radiation hardened

We have we have a CUDA in satellites around the world They're doing imaging image processing AI imaging and and that kind of stuff out of be done in space Instead of sending all the data back here and do imaging down here. We ought to just do imaging out on space and so there's a lot of things And we ought to do do in space and in the meantime

We're gonna explore what is the architecture of data centers look like In space and it'll take it'll take years. It's okay. I got plenty of time I wanted to double click on health care. I know you've got a big effort there We're all of a certain age where we're thinking about life span health span. I mean we all look great. I think

“Better than I think some better than others. I don't know what your secret is Jensen pretty good. I mean”

What's taking age? What's off to men? You got to talk to me when we're backstage. I want to know in the green room What you got going on squads and push-ups and stuff. Perfect. Yeah, but What you know in terms of the buildout in health care Where is that going and what kind of progress are we making? I was just using clawed to do some analysis and saying like we're all these Billing codes. We spend twice as much money in the U.S. We get seem to get half as much. It seemed like

15 to 25% of the dollar spent were on these first GP visits and I think we all know like chat GBT and a large language model does a better job more consistently today at a first visit So what has to happen there to kind of break through all that regulation and have AI have a true impact on the health care

Physics and and that's or AI biology using AI to understand Represent predict biology behavior biological behavior and so that's one that's very important in drug discovery

There's second which is AI agents and that's where the assistance in helping diagnosis and things like that

Open evidence is a really good example. Hippocratics is a really good example. Love working with those companies

“I really think that this is an area where agent technology is going to revolutionize how we interact with doctors and how do we interact”

for health care the third part that we're involved in is physical AI the first one's AI physics using AI to predict physics the second one is physical AI AI that understand the properties of the laws of physics and that's used for robotic surgery huge amounts of activities there every single instrument whether it's ultrasound or you know CT or whatever instrument we interact with in a hospital in the future will be agentic

Yeah, you know open claw in a safe version will be inside every single instrument and so in a lot of ways That instrument's going to be interacting with patients and nurses and doctors. Yeah, I mean very So much investment in AI we'll be wonderful to see some investment in AI EMTs and paramedics and saving lives Not just taking them. Yeah, which I think is a great segue into robotics. You've got dozens of partners. Yeah, we have this very weird I don't know I want to call a lost decade or 20 years of Boston dynamics google bought a bunch of

companies they then wound up selling them and spinning them out where people just thought That robotics is just not ready for prime time and now here we have the world's greatest entrepreneur at this time tied with you Elon Musk doing well. That was a good save. I hope optimism pretty impressive and then other companies in China how how close is that to actually

being in our lives where we might see a chef a robotic chef a robotic nurse a robotic housekeeper You know these humanoid factor actually working in the real world knowing what you know with those partners and the fidelity especially in China where they seem to be doing as good a job as we're doing here or maybe better We invented the industry largely American invented we could you could argue we got into a too soon Yeah, and and we got exhausted we got tired

about five years before the enabling technology of the brain. Yeah, yeah, and we just got tired of it just a little too soon. Okay, that's number one, but it's here now. Now the question is how much longer from the point of high functioning existence proof high functioning existence proof to reasonable products

Technology never takes more than a couple two three cycles and so a couple two three cycles and basically be somewhere around three years the five years that's it

“Three years the five years we're going to have robots all over the place. I think I think”

China is is a formidable and the reason for that is because their micro-electronics, their motors, their rare earth, their magnets which is foundational to robotics they are the world's best and so in a lot of ways our robotics industry relies deeply on their ecosystem and their supply chain and and and there you know obviously moving very quickly We're going to you know our robotics industry will have to rely a lot on it the world's robotics industry will have to rely on a lot of on it

and so so I think you're going to see some fast fast movements here ultimately one for one Elon seems to think we're going to have one robot for every human

7 billion for 7 billion a billion for 8 billion well I'm hoping more yeah I'm hoping more yeah

well first of all there's a whole bunch of robots that are going to be in factories working around the clock there's going to be a whole bunch of fact robots that that don't move they move a little bit

“almost everything will be robotic what is that I think I think like this is one of the robotics for me is one of the pieces that I think”

unlocks economic mobility opportunities for every individual everyone now like when everyone got a car they could now go and do a lot of different jobs when everyone gets a robot their robot robot could do a lot of work for them they can stand up in Etsy store a Shopify store they can create anything they want with their robot they could do things that they independently cannot do

I think the robot is going to end up being the greatest unlock for prosperity for more people on earth than we've ever seen with any technology before yeah no doubt I mean just as simple math at the moment is we're millions of people short in labor today right we're actually really desperate and need of robotics and so that all of these companies could grow more if they had more labor I mean

we'll have virtual presence you know I'll be able to go into the robot of my house

and virtually operated i'm on a business trip right tell me I walk around the house yeah walk the dog yeah we'll have to break the leaves to your exact recap of the dog maybe not quite bad but just you know just you know wander around and just see what's going on in the house you know chat with the dog chat with the kids yeah yeah and I'm travel is also we're going to be able to travel to speed a light you know and so you know clearly when it's sent our robots ahead of us

yeah I'm not going to send myself I'm going to send a robot you know check it out yeah yeah and then I'm going to upload my AI well it's inevitable it unlocks the moon and it unlocks Mars as um target for for colonization which gives us infinite resources getting back from the moon of is effectively zero energy cost to move material back because you can use solar and accelerate so you could have factories that make everything the world needs on the moon and the robot

they're going to be the unlock for a neighbors right this house distance no longer matter distance doesn't matter yeah yeah the more the more revenue we get out of models and agents the more we can invest in building the infrastructure which then unlocks more capabilities on models and agents Dario on door cashiest podcast recently said by twenty seven twenty eight well of hundreds of billions of dollars of revenue out of the model companies in the agent companies

and he forecast a trillion dollars by twenty thirty right this is non-infrastructure AI revenue

“I think he I think he he's being very conservative I believe Dario and then Thropic”

is going to do way better than that way better than that wow so 30 billion to a trillion yeah

and not and and the reason for that is the one part that he hasn't considered is that I believe every single enterprise software company will also be a reseller value at a reseller of then Thropic code and Thropic's tokens value at a reseller open AI have to value that's right yeah they're gonna that that that part of their get this logarithmic expansion yes yeah there go to market is going to expand tremendously this year what do you think in that world

is the mode what's left over let me you have some modes that are frankly I think as this scales almost instrumental the best one that nobody talks about is probably kuda which is just like in the incredible strategic advantage but in the future if a model can be used to create

something incredible then the next spin of a model can be used to maybe disrupt it sort of in

your mind what do you think for these companies that are building at that application layer what's

“their mode like how do they differentiate themselves deep specialization deep specialization I believe”

that these models that they're gonna have general general models that are connected into the software company's agentic system right many of those models are cloud models and proprietary models but many of those models are specialized sub agents that they've trained on their own right so look all the arms for you for entrepreneurs is look know your vertical that's right know it as deep and as better than everybody else that's right and then wait for these tools

because they're catching up to you and now you can imbue it with your knowledge that's right in your center you connect your agent the senior you connect your agent with customers that fly wheel it's gonna cause your agent to get it really very much isn't a version of what we do today because today we build a piece of software and we say what generalizes and then let's try to sell it is broadly as possible and then sell the customization around it and we in fact in fact with exactly

where we we create a horizontal but notice they're all these GSI's and all of these consultants who are specialists who then take your horizontal platform and specializes it into exactly and that's arguably a five or six time bigger industry is the customization it is absolutely yeah that's right so

“I think that these platform companies have an opportunity to become that specialist to become that”

vertical right yeah domain X yeah I just want to give you your flowers I think it was three years ago you said you're not going to lose your job to AI you're going to lose your job to somebody using AI and here we are the entire conversation has revolved around this concept of agents making people superhuman and the business opportunity expanding an entrepreneurship expanding you actually saw it pretty clearly yes right well I know I do have I you can hold space for I think two ideas one is there

are going to be a liberal Jacob I don't know there you can but that's just because he doesn't hang out with me you know I mean we found a little bit he careful I'm not asking for that I'm not asking for that I'm not asking for that I'm not asking for you you come to me and Tucker we ski in Japan every January I'll love it we are Tucker we'll go road trip there is going to be a job displacement and then the question becomes you know do

going to see a hundred percent of driving go away by humans that's just it's that's a beautiful

thing in the lives saved but we have to recognize that's 15 million people in the United States

“10 to 15 million who are employed in that way and so that is going to happen yes I think I think”

that jobs will change for example there are many chauffeurs today who drives the car I believe that many of those chauffeurs will actually be in the car sitting behind the driving wheel while the car is driving by itself and the reason for that is because remember what a chauffeur does in the end these chauffeurs they're helping you they're your systems they're helping you yeah your luggage they're helping you I mean they're helping you with a lot of things and and so I wouldn't be surprised actually

if the chauffeurs of the future become your mobility assistant and they are helping you do want a whole bunch of other stuff in the hotel and the car is driving by itself and so autopilot planes create it a lot more pilots yeah and didn't take any of the pilots out of the cockpit yeah

right the autopilot is flying the plane 90 percent of it and by the way while that car is driving

itself that chauffeur is going to be doing a bunch of other work on his phone and he's going to be a range for example doing an orienting a bunch of things for you getting you know yeah it's all the pilot's growth in a way yeah so one of the things that that that yes every job will be transformed some jobs will be eliminated however we also know that many many jobs will be recreated yeah the one thing that I will say to young people who are coming out of school who are concerned who are

anxious about AI be the expert of using AI yes how much look yeah we all want our employees to be expert at using AI and it's not not not trivial not trivial and so knowing how to specify not to over prescribe leaving enough room for the AI to innovate and create while we guided to the outcome we want if all of that requires artistry you had you had this great advice to when you were at Stanford I

“think it was which is I wish to you pain and suffering remember that fantastic what's your advice”

to young people around what they should be studying so if they're sort of about to leave high school because now those are the kids that are at this like really native they haven't made a decision about college what to study if at all go to college how do you guide those kids what would you tell them I I still believe that deep science deep math uh language skills you know as you know languages the programming language of AI ultimate program yeah and so as it turns out it could be that the

English major could be the most successful yeah and and so so I think I think I think I would just advice whatever whatever education you get just make sure that you're deeply deeply expert in using AI's one of the things that I wanted to say with respect to jobs and I want everybody to hear it that in fact at the beginning of the deep learning revolution one of the the finest computer scientists

“in the world deeply deeply deeply deeply uh respect uh predicted that computer vision will”

completely eliminate radiologists and and that the one at the one field he advises everybody to not going to is radiology ten years later his prediction was at a hundred percent right computer vision has been integrated into all of the radiology technologies and radiology platforms in the world a hundred percent the surprising outcome is the number radiologists actually went up and the demand for radiologists is skyrocketed the reason for that is because everybody's job has a purpose

and it's task the task that you do is studying the scans but your purpose is to die and help the doctors help the patient diagnose disease and so what surprising is because the scans are now being done so quickly they could do more scans improving health care yes but doing more scans more quickly allows patients to be onboarded a lot more quickly treated a lot more quickly and as it turns out because hospitals enjoy making money too yeah right they're doing more scans

feeding more training more customers and early detection becomes the revenues going perfect yes what perfect yeah yeah and a country that grows faster productivity increases a wealthy our country can put more teachers in the classroom not less teachers in the classroom that's right you just give every one of those teachers a personalized curriculum for every student in the room it makes them all bionic and leads to a lot more every single student will be assisted by AI

but every single student will need great teachers yeah amazing uh jensen congratulations on

your success and really this is an incredibly positive uplifting discussion we really appreciate

more vocal I'm being very very vocal about the positive side of it I think there's so much

numerism but I also think it takes the humility to have this level of success and be humble about

“we're making software guys yeah and I think that that's actually really healthy for people to”

hear we have done this before we have invented categories and industries before yes we don't

need to go to this scare mongering place it does nothing and we get to choose right we have autonomy

“and agency we get to pick out of it we sure do for this okay everybody we'll see you next time”

thank you on the all in interview okay well thanks man good job thank you sir that was awesome

good good you guys are awesome thanks look at this this is a big crowd behind you guys yeah I I think they're here for you