NVIDIA’s Jensen Huang on Reasoning Models, Robotics, and Refuting the “AI Bubble” Narrative

Hson, thanks so much for joining us today.

>> So great to have you guys. What an

amazing year.

>> What a year.

>> Happy Hanukkah, merry Christmas, >> happy new year coming up. Yep. Happy

holidays.

>> So, uh, with everything that's happened in 2025, um, and you know, being in the middle of the vortex with it, what do you reflect on and say like this surprised you most or this is the biggest change?

>> Let's see. There there's some things that didn't surprise me like for example the scaling laws didn't surprise me because we already knew about that. The

technology advancement didn't surprise me. I was pleased with the improvements

me. I was pleased with the improvements of grounding. I was pleased with the

of grounding. I was pleased with the improvements of reasoning. I was pleased with uh uh the connection of all of the models to to to search. I'm pleased that

it that uh there are now routers that are in front of these models so that it could depending on the confidence of the answers go off and do necessary research

and and just generally improve the quality and the accuracy of answers.

>> I'm hugely proud of that. I think the whole industry addressed one of the biggest skeptical responses of AI which is hallucination and um generating

gibberish and all of that stuff. I I

thought that this year the whole industry everything from every every field from language to vision to robotics to self-driving cars the the

application of reasoning and the grounding of the of of of the answers. Um big big leaps would you guys

answers. Um big big leaps would you guys say this year?

>> Huge. I mean things like open evidence too for medical information where doctors are now really using that as a trusted resource like you Harvey for legal you're really starting to see AI emerge as one of these things that's

become a trusted tool or counterparty for you know experts to actually be able to do what they do much better.

>> That's that's right. And so so in a lot of ways I was expecting it but I'm still pleased by it. I'm proud of it. I'm

proud of all of the industry's work in this area. I'm really pleased and and uh

this area. I'm really pleased and and uh uh and probably a little bit surprised in fact that token generation rate for

inference especially reasoning tokens are growing so fast several exponentials at the same times it seems and uh and I'm so pleased that that these tokens

are now profitable that people are generating I heard somebody hurt today that that open evidence speaking of them 90% gross margins I mean those are very profitable tokens.

>> Yeah.

>> And so they're obviously doing very profitable, very valuable work. Cursor,

their margins are great. Uh Claude's

margins are great for the enterprise use of OpenAI. Their margins are great. Um

of OpenAI. Their margins are great. Um

so anyways, it's really terrific to see that that um we're now generating tokens that are sufficiently good, so good in value that that people are willing to pay good money for. And so I I think

these are are really great grounding for the year. I mean some of the things that

the year. I mean some of the things that the narrative that that um uh of course the conversation with China really really you know occupied a lot of my my

time this year. Geopolitics

uh the importance of technology in each one of the countries. Uh I spent more time traveling around the world this year than just about any time in the h all of my life combined. You know my average elevation this year is probably

about 17,000 ft. You know so so it's nice to be here on the ground with you guys. Um and so so I think uh

guys. Um and so so I think uh geopolitics the importance of AI to all the nations uh all worth talking about later. You know of course I spent a lot

later. You know of course I spent a lot of time on expert control and and making sure that our strategy is nuanced and uh really grounded and um uh promotes

national security but recognizing the importance of various uh various facets of national security. Um a lot of conversations around that. Um, you know,

of course, of course, uh, lots of conversation about jobs, the impact of AI uh energy >> um, uh, labor shortage. I mean, boy, we covered everything, did we? Yeah.

Everything was AI.

>> Everything was AI. Yeah, it was incredible.

>> Yeah, AI was definitely the center of the storm for like every one of those themes. Maybe one we can start with

themes. Maybe one we can start with actually um is jobs because or there jobs and employment because when I look at the traditional AI community even before things were scaling and even

before AI was really working there was a strong sort of doomsday component in the people working on AI oddly enough right the people who were most trying to push the field forward were often the people who are most pessimistic which is very odd why would you do both at once

>> and I feel like that narrative has taken over some subset of media or some set of other things despite all the things that we think are very positive about what AI has done That's going to help with healthcare, with education, with productivity, with all these other

areas.

>> And in in general, whenever we have a technology shift, you have a shift in terms of the jobs that are important, but you still have more jobs.

>> That's right.

>> Could you talk about how you think about employment and jobs and sort of what people are saying and what you think the real narrative is there?

>> Maybe what I'll do is I'll I'll ground it on uh three points in space, three points in time. now.

>> Mhm.

>> Uh maybe uh uh very near future and then some some point out out in the distance and and maybe maybe some counternarratives.

>> Um something else to think about with respect to jobs in the near term.

>> Uh one of the most important things is that that AI is not just AI is software >> but it's not pre-recorded software as you know. For example, Excel was written

you know. For example, Excel was written by several hundred engineers. They

compiled it. It's pre-recorded and then they distribute it as is for several years. In the case of AI, because it

years. In the case of AI, because it takes into the context, what you asked of it, what's happening in the world, right? Contextual information, it

right? Contextual information, it generates every single token for the first time, every time.

>> Which means every time you use the software and and everything that we do, AI is being generated for the first time ever. Just like intelligence, our

ever. Just like intelligence, our conversation today relies on some, you know, ground truth and some knowledge and but it's every single word is being generated for the first time here. The

thing that's really really quite unique about AI is that it needs these computers to generate these tokens every single time. I call them AI factories

single time. I call them AI factories because it's producing tokens that will be, you know, used all over the world.

Now, some people would say it's also part of infrastructure. The reason why it's infrastructure is because obviously it affects every single application.

It's used in every single company. It's

used in every single industry every single country. Therefore, it's part

single country. Therefore, it's part infrastructure like energy and and internet. Now, because of that and the

internet. Now, because of that and the amount of computers that's necessary to generate these tokens and it's never happened before and because we need these factories, three new industries have emerged. Number one, well, three

have emerged. Number one, well, three new type of plants have to be created.

Number one, we have to build a lot more chip plants.

>> Mhm. TSMC is building, right? SKH Highix

building a lot more plants and so we need more chip plants. We need more computer plants. These computers are

computer plants. These computers are very different. These are supercomputers

very different. These are supercomputers that the world's never seen before.

Right. Grace Blackwell looks like a very different type of computer than anything that's ever been made. And entire rack is one GPU.

>> And so we need new supercomputer plants.

And then we need new AI factories. These

three plants are currently being met being built in the United States at very large scale quite broadly all over the United States for the very first time.

>> The number of construction workers, plumbers electricians technicians network engineers, you know, right? The

the number of the skilled labor that's necessary to support this new industry in the near term, >> it'll be enormous. Let's just face it.

Uh I'm [clears throat] so excited to hear that electricians are seeing their paychecks double. They're being they're

paychecks double. They're being they're being paid to travels like like us. We

go on business trips. They're going on business trips. And so it's really

business trips. And so it's really terrific to see, you know, that this these three industries are now three types of plants, factories are just

creating so much so much jobs. The next

part is the the near-term impact of AI on jobs. And one of my favorites is um I

on jobs. And one of my favorites is um I love Jeff Hinton. uh he said uh you know some five six seven years ago that in

five years time uh AI will completely revolutionize radiology that every single radiology application will be

powered by AI and that radiologists uh will no longer be needed and that he would advise this the first profession not to go into is radiology and he's

absolutely right 100% of radiology applications are now AI powered. That's

completely true and in some eight years time it is now completely pervaded uh uh radiology. However, what's interesting

radiology. However, what's interesting is that the number of radiologists increased >> and so now the question is why and this is where the difference between task

versus purpose of a job. A job has tasks and has purpose. And in the case of a radiologist, the task is to study scans,

but the purpose is to diagnose disease >> and to research >> and and that exactly and they're doing research. And so in the case in their

research. And so in the case in their case, the fact that they're able to study more scans more deeply, um they're able to uh request more

scans, do a better job diagnosing disease, the hospital's more productive, they can have more patients, which allows them to make more money, which allows them to want to hire more

radiologists. And so the question is

radiologists. And so the question is what is the purpose of the job versus what is the task that you do in your job? And and as you know, I spend most

job? And and as you know, I spend most of my >> day typing. [snorts] That's my task, but my purpose is obviously not typing. And

so the fact that somebody could use AI to automate a lot of my typing, and I really appreciate that, and it helps a lot.

>> Um, it hasn't really made me, if you will, less busy. In a lot of ways, I become more busy because I'm able to do more work. So, I think that that's the

more work. So, I think that that's the second part to consider is the task versus the purpose of the job. This

example really strikes home because my my sister-in-law Erin actually leads um in nuclear medicine at Stanford, right?

So, she's in radiology and with all the technology advancements that are coming, >> these doctors really welcome it and they are working 20 hours a day trying to do more research and serve more patients.

Exactly. And and I think one thing that is often missed beyond the sort of um uh diversity of jobs being created by this investment in infrastructure is actually

how much latent demand there is for different goods that we we need in society like better healthcare. I don't

think anybody feels like you know what we have reached the the tiptop uh mountaintop of like what American healthcare or global healthcare could be

and um the more we can make these people productive the more demand there will be >> that's exactly right if I if Nvidia was more productive it doesn't result in layoffs it results in us doing more more

things >> I met your new hire class today you seem to be hiring every week anyway yeah >> that's exactly right right the the more productive we are the more uh ideas we

can explore uh the more growth as as a result the more profitable we become which allows us to pursue more ideas and so I think you're you're absolutely

right that that if if the job if if your if your life if the world the problems is literally already specified and there's no other problem to solve then

productivity would actually reduce the economy but it's clearly going to increase the e economy I think that the Next part that I would consider is, you know, people say, gosh, all of these

robots that we're talking about, it's going to take away jobs. As as we know very clearly, we don't have enough factory workers. Our economy is actually

factory workers. Our economy is actually limited by the number of factory workers we have. Most people are are having a

we have. Most people are are having a very hard time retaining their workers.

Um, we also know that the number of truck drivers in the world is severely short. And the reason for that is people

short. And the reason for that is people don't want those jobs where you have to travel across the country and live in different parts of the world, different parts of the country, you know, every single night. So people want to stay in

single night. So people want to stay in their town, stay with their families.

And so I think that I think the first part is that having robotic systems is going to allow us to cover the labor shortage gap which is really really

severe and getting worse because of aging population. This is this is not

aging population. This is this is not only United States, it's all over the world as you guys know.

>> And so we're going to cover the labor shortage. But the second part that

shortage. But the second part that people forget and and as a result we'll go there are shortages as well in other places that people talk about AI being relevant. Accounting would be an example

relevant. Accounting would be an example where there's shortages there. Nursing

is another example. So you know you can you can go through multiple other industries and say okay there's gaps right >> and AI is trying to help fill those gaps.

>> That's exactly right. And so so um automation is going to help us increase and solve the the the the labor gap. Now

people also don't don't remember that when we have cars, we need mechanics to take care of our cars.

>> And if you look at the robo taxis that are that are even on the streets today, it's taken 10 years for that to happen.

Look at all the maintenance crews and all of the the the various, you know, hubs that they're in where you have to take care of these robo taxis and just imagine we have a billion robots.

>> Mhm.

>> It's going to be the largest repair industry on the planet. So I I think a lot of people don't they they just have to think through >> and this is the part where you said um when we create this type of automation,

we create this other job. Right now look at AI is creating so many jobs. Mhm.

>> The AI industry is creating a boom of jobs.

>> I think one of the core challenges here is it's very easy to draw a straight line of extrapolation from like oh you know uh there are tools that help lawyers be more productive. It's going

to replace the lawyers but it's actually it takes like a step of incremental reasoning to say there's a sucking sound in the economy for everything in AI infrastructure. there's actually a

infrastructure. there's actually a sucking sound toward all of this demand that is latent in the places where we have gaps where um I think a lot of policy makers have focused on you know

we can't replace or reduce what we have when it's really there's there's far more demand in what we actually are not >> and in the case of lawyer what's the what's the purpose of the lawyer versus

the task of the lawyer >> reading a contract writing a contract is not the purpose of the lawyer the purpose of the lawyer is to help you resolve conflict

And that's more than reading a contract.

It's more than writing a contract. The

purpose is to protect you. That's more

than reading a contract. It's more than writing a contract. And so I think just it's really really important to go back to what is the purpose of the job versus the task that we use,

>> you know, to perform that job that changes over time.

>> Yeah. The other big theme of the year that you mentioned that I think is really important to touch upon is both uh China is sort of in the rise of Chinese open source in particular where you know some of the highest scoring models against benchmarks now are

Chinese models on the open source side on the closer side it's still a lot of the US models but things like Quinn Deepseek etc >> are doing very well you've long been a proponent for open source in general could you could you share views about

both China emerging for AI for open source and what the US should be doing in terms of both open source as well as its own industries >> when you Think about these complicated

interconnected dependent um networks of problems. These this you know big goop of a mesh of problems it's always good to to go back and find a

framework for what it is that we're talking about. In the case of AI um what

talking about. In the case of AI um what is AI?

Well, of course, the technology of AI and the capability, the capabilities of AI is about automation. It's about

automation of intelligence for the very first time. And you could combine it

first time. And you could combine it with megatronics technology to embody that megatronics and and make it perform tasks.

>> So that's what's AI automation. But what

what is the stack that makes AI possible? What's the technology stack?

possible? What's the technology stack?

functional stack. And of course the e the easiest way to think about that is it's kind of like a fivey year five year five layer cake which is at the lowest level is energy.

>> Um it transforms energy to the output that I just described. The next layer is chips. The next layer is infrastructure

chips. The next layer is infrastructure and that infrastructure is both hardware software right this is where land power and shell this is where construction is data centers are the software stack

>> you know for orchestrating the so it's software and hardware the layer above that is where everybody thinks about which is AI which is the models >> we know this but it's really helpful to

understand that AI is a system of models >> and AI is a um a techn technology that understands information and there's

human information and so we often times think about AI as a chatbot >> but remember there's biological information there's chemical information there's physical information of all

kinds there's financial information there's healthcare information there's f there's information of all modalities all kinds AI is really really broad and of course human language is at the

foundation of of many things but it's not the essence of everything because as you know you know biology molecules don't understand English >> they understand something else right proteins don't understand English they

understand something else I think the next layer the important thing is is uh that's where the AI models are but there's a whole the AI is very very diverse and then the the layer above that is is applications and it depends

on the industry and you already mentioned open evidence there you mentioned Harvey there's cursor there's all kinds of right there's all kinds of applications full self-driving is really an application, an AI application that

is embodied into a mechanical car >> and figure is a AI application that has been embodied into a mechanical human.

And so, so you got all these different applications. Well, this five layer

applications. Well, this five layer stack is one way of thinking about it.

And then the next way of thinking about I just mentioned is AI is really diverse. When you now have this

diverse. When you now have this framework of what the the technology capabilities are, how to how to build the technology and how diverse it is,

then you can come back and think about okay, let's ask the question, how important is open source? Well, without

open source, you know, today, of course, the frontier models, the the the leading labs have chosen to to use a closed source um application approach, which is

just fine. you know what people decide

just fine. you know what people decide to do with their business models is is really in the final analysis. It's their

business and they have to they have to calculate what is the best way for them to get the return on investment so that they could scale up and and make better advances. Um however they they made that

advances. Um however they they made that calculus is fantastic. On the other hand, uh without open source, as you know, startups would be challenged, uh

companies that are in in uh uh different industries, whether it's manufacturing or transportation or um it could be in healthcare. Without open source today,

healthcare. Without open source today, all of that AI work would be suffocated.

>> And so, they just need to have something that's pre-trained. They need to have

that's pre-trained. They need to have some fundamental technology about reasoning. from that they could all

reasoning. from that they could all adapt, fine-tune, you know, train their AI models into exactly the domain and application they want. And so what

people really really miss is just the incredible pervasiveness and the importance of open source to all of these industries. large companies uh

these industries. large companies uh without without open source some of some of 100-year-old companies that I work with >> in in industrial spaces in healthcare spaces they would be suffocated they

wouldn't be able to do that >> open source at this point is driving all of our data centers is driving a big chunk of telefan in the world in terms of Android or other devices it's driving exactly >> you know to your point a lot of the industrial applications so it's already

pervasive and I think the big question is >> open source without open source higher ed >> higher ed wouldn't happen >> education research >> startups I mean the list goes on, you know, and so so

>> we talk we talk all day long about the tip but the most visible part of that the most the part that's most newsworthy maybe but underneath that is such an

important space of open source AI and whatever we decide to do with policies do not damage that innovation flywheel.

So I spent a lot of time uh educating educating uh uh policy makers to help them understand whatever you decide whatever you do don't forget open source. Whatever you decide whatever you

source. Whatever you decide whatever you do don't forget biology.

I think the counternarrative here that is worth addressing is that essentially like you know there should be a monolithic vertical player and

monolithic asset in the like one model that does it all and that we can't give away that crown jewel to other countries or non-American companies and and your your argument is like we actually need

this huge diversity of AI applications and and the American advantage is actually or any any sovereign advantage is in the whole stack right? The

capability to deliver any piece of it.

>> I guess someday we will have God AI.

>> But when is that day?

>> But but that someday that someday is probably on biblical scales, you know, I think galactic scales. Um I I think it's it's not helpful to go from where we are

today to God AI.

>> And um I don't think any company practically believes they're anywhere near God AI. And nor nor do I do I see any researchers having any reasonable

ability to create god AI. The ability to h understand human language and genome language and molecular language and protein language and amino acid language

and physics language all supremely well.

That god AI just doesn't exist.

>> And and yet we have a lot of industries that need AI. Mhm.

>> AI is if if you will at the simplistic level, it's just the next computer industry.

>> And give me an example of a company, an industry, a nation who doesn't need computers.

>> Mhm.

>> And we all don't have to wait around for God AI for us to advance, right? So God

AI is not showing up next week. I'm

fairly certain of that. Okay. And God

[clears throat] AI god AI is not not going to show up next year, but the whole world needs to move forward next week, next year, next decade. I think

that that the idea of a monolithic gigantic company, >> country, nation, state that has got AI is just

>> it's unhelpful.

>> It's unhelpful. It's too extreme.

>> Then in fact, if you want to take it to that level, then we ought to just all stop everything.

What's the point of having even governments? I mean, why why why are

governments? I mean, why why why are they doing policies? God AI is going to be smart enough to avert, you know, work around any policy. And so, what's the point? And so, I I think that that we

point? And so, I I think that that we ought to bring things back to the ground ground level and start thinking about things practically and and use common sense.

>> This seems to be like a big theme in general in terms of this conversation where there's been a lot that's been kind of put out there that seems very extreme if you actually think about it.

It's the jobs and employment. Nobody's

going to be able to work again. It's God

AI is going to solve every problem. It's

we shouldn't have open source for XYZ reason despite open source powering much of our industries already.

>> That's right.

>> And so it seems like in general maybe one of the themes of 2025 was there's a lot of extremes that were sort of painted in the public with AI that if you look at them very closely don't really follow a logical change in terms of happening anytime soon.

>> Yeah. And so it's it's it sounds like it's really important to have this conversation.

>> Extremely hurtful frankly. And I I think we've done a lot of damage uh with very wellrespected people um who have who have painted a doom doomer narrative um

end of the world narrative science fiction narrative and um you know and I and I appreciate that that many of us grew up in and enjoyed science fiction.

>> Um but I but it's not helpful. It's not

helpful to people. It's not helpful to the industry. It's not helpful to

the industry. It's not helpful to society. It's not helpful to the

society. It's not helpful to the governments. Mhm.

governments. Mhm.

>> There are a lot of many people in the government who obviously aren't as familiar with as as comfortable with the technology

>> and when PhDs of this and CEOs of that >> goes to governments and explain and describe these end of the world scenarios and extremely extremely

dystopian future the future. Um, you

have to ask yourself, you know, what is the purpose of that narrative and what is their what are their intentions and what do they hope? Why are they why are they talking to governments about these

things to create regulations to suffocate startups? [clears throat]

suffocate startups? [clears throat] >> For what reason would they be doing that, you know, and so >> and do you think that's just regulatory capture where they're trying to prevent uh new startups from showing up and being able to compete effectively or

what do you think is the goal of some of these conversations? you know, I I can't

these conversations? you know, I I can't I can't um uh guess what they what they have in mind. I know that the concern is

regulatory capture. As a policy, as a

regulatory capture. As a policy, as a practice, I don't think companies had to go to

um governments to advocate for the regulation on other companies and other industries. just in practice

their their intentions are clearly deeply conflicted and and uh their intentions are clearly you know not completely in the best interest of

society. I mean they're obviously CEOs

society. I mean they're obviously CEOs are obviously companies and obviously they're advocating for themselves >> and so so I think if we can all

>> come back to where are we today >> and think about where the technology is going to be. I mean look lit literally in one year's time as we were talking

about in the beginning uh some of the most proud moments is when the industry was able to invest very aggressively in advancing AI technology instead of being

slowed down.

>> Remember just two years ago people were talking about slowing the industry down >> but as we advanced quickly what did we solve? We solved grounding, we solved

solve? We solved grounding, we solved reasoning. We solved research. All of

reasoning. We solved research. All of

that technology was applied for good improving the functionality of the AI not you know >> yet the end has not come >> yet the end has not come it's become

more useful it's become more functional it's become able to do what we ask it to do you know and so the first the first part of the safety of a product is that

it perform as advertised >> the first part of safety is performance that it's is supposed like the first part of safety of a car isn't that some

person is going to jump into the car and use it as a missile. The first part of the car is it works as advertised.

>> Mhm.

>> 99.999% of the time working as advertised. And

so it takes a lot of technology to make that car or make that AI work as advertised. And I'm really glad that in

advertised. And I'm really glad that in the last couple two three years the industry has invested so much in enhancing the functionality of the AI as advertised. And I think if if we were to

advertised. And I think if if we were to to look at the next 10 years, we have so much work to do to make it work as advertised. Meanwhile, as as you know,

advertised. Meanwhile, as as you know, you both of you invest so much in in the in the ecosystem, you see so many companies being built for um synthetic

data generation so that the AIs could be more grounded uh more diverse uh less biased more safe uh you're investing in a whole bunch of companies in cyber

security using AI for cyber security you right people think that there's this AI um the marginal cost of AI is going to go go down significantly and it is

>> and therefore the AI is going to be dangerous. It's exactly the opposite. If

dangerous. It's exactly the opposite. If

the marginal cost of AI is going to go down significantly, that one AI is going to be monitored by millions of AIS.

>> Mhm.

>> And more and more AI is going to be monitoring monitoring each other. People

don't can't forget that an AI is not going to be an agent by itself. It's

likely the AI is going to be surrounded by agents monitoring it. And so it's no different than if the if the marginal cost of of keeping society safe was

lower. We have police in every corner.

lower. We have police in every corner.

>> So one thing that that we were talking about a little bit earlier was just the cost of AI and how it's been coming down. And so

down. And so >> I I think um in 2024 the the cost of GPT4 equivalent models if you look at a million tokens it came down over 100x.

Um you know somebody in my team did this analysis to show that. Uh so the costs are dropping pretty dramatically and very rapidly and part of it is all the advancements you all have been driving on and the Nvidia level but also just across the stack getting big efficiency gains.

>> Yeah.

>> Um at the same time model companies are talking about how the costs are rising how there's enormous sort of capital modes to building these things out. How

do you think about cost of training and cost of inference over time and what that means for the average end user or the average startup company trying to compete or people trying to do more in this industry? I forget the statistic

this industry? I forget the statistic that but but you know Andre Andre Cararpathy um estimated the cost of building the first chatbt I think >> versus now I think you could do that on the PC now.

>> Yeah. Yeah. It's probably tens of thousands of dollars at this point or maybe even less.

>> Right. And so it costs nothing.

>> Mhm.

>> And and >> he has an open source project that you can do in a weekend.

>> Oh, is that right? Okay. That's

incredible. Right. We're talking about three years. Mhm.

three years. Mhm.

>> Mhm.

>> What people people said cost billions of dollars um supercomputers built raising billions of dollars in order to do all that now

>> cost you know something that you can do on a weekend on a PC. And so that tells you something about how quickly we're making making AI more cost effective >> or Spark sorry probably not quite a PC.

>> Okay. Not quite a PC. Yeah. We're

improving our architecture and performance um every single year. The first GBTU I think was trained on Voltus.

>> Mhm.

>> And then uh Ampear um you know and and it wasn't I think the first breakthroughs none of it included Hopper.

>> Mhm.

>> And um of course Hopper the last couple two three years and um uh we're off in Blackwell for the last year and a half or so. And um every single one of these

or so. And um every single one of these generations the architecture improves and of course the number of transistors go up and uh the capacity goes up every

single generation very easily every every single year from a computing perspective. The combination of all that

perspective. The combination of all that getting 5 to 10x every single year >> is not unusual. And here comes Reuben just around the corner. And so we're

seeing 5 to 10x every single year. Well

compounded it's incredible. Moore's law

was two times every year and a half >> and over the course of five years is 10x over the course of 10 years is 100x >> in the in the in the case of AI over the course of 10 years is probably 100,000

to a millionx okay and that's just the hardware >> then the next layer is the algorithm layer and the model layer the combination of all that the fact that if

you were to tell me that in the cost in the in the in in the span of you know 10 years we're going to reduce the cost of token generation about a billion times.

I would not be surprised.

>> Mhm.

>> Okay. And so that's the tokconomics of of of AI. On the training side, it's not quite as aggressive in in cost reduction, but it's close. If you were

to say that that every single year we're increasing by two or 3x over the course of 10 years, incredible. But the

important idea is when somebody says it cost $und00 million to train something or half a billion dollars to train something.

Well, next year it's 10 times less. Next

year it's 10 times.

>> For people to scale these things up, though, right? So the counter argument

though, right? So the counter argument is, well, we'll just get bigger every year by 10x or 100x or, you know, we'll try to offset that decrease in cost by scale >> and others can't keep up.

>> Yeah. But really what's happening is is you're and and this is where come in as you know the scale went up by a factor of 10 but the computational burden did not go up by a factor of 10 because

you're getting the compounded benefits of all three things. The hardware is going up the the algorithms of the training models are going up and of course the model architecture is going up and we're getting the benefit of

learning from each other. This is, you know, let's face it, Deep Seek was probably the single most important paper that most Silicon Valley researchers

read from in the last couple years.

>> It was the only thing that felt frontier that was open.

>> That's right.

>> In years, the value of open source again putting out these papers.

>> Literally, Deep Seek >> benefited American startups and American AI labs all over >> and infrastructure companies >> and infrastructure company all over.

probably the single greatest contribution to American AI last year.

>> And so if you said this out loud, of course, you know, people >> kind of shudder um that we're uh American AI is actually getting learning

from and benefiting from uh AI from other nation. But why would that be

other nation. But why would that be surprising? You know, AI researchers in

surprising? You know, AI researchers in all over America, all over America are uh Chinese natives and come from different countries. We benefit from

different countries. We benefit from every country. become benefit from every

every country. become benefit from every researcher and no all of the world's ideas don't have to come from the United States and so I I think um back to your

your original question it is the case that you know some of the narratives around around the cost of AI is about scaring everybody out of the market you know

nobody ought to do pre-training but us nobody should do you know training these frontier models but us because the because of innovation of models algorithms

and the computing stack, the cost of AI is actually decreasing well more than 10x every single year. And so if you're just one year behind or even six months behind, you could you could really stay close.

>> And I think one thing that felt very different to me about 2025 is um Ilia uh said recently that uh you know we're in the age of research again versus an age of scaling. I think both things are

of scaling. I think both things are happening by the way. Everybody is also trying to scale on multiple dimensions.

>> Yeah, exactly. both are happening.

>> You know, being 6 months behind or being at 100 versus a 200k cluster, I think matters if you are competing symmetrically, but now you have people from Frontier Labs or um at the very top

of the game who have very different ideas about how to progress from here or who are working on diversity of problems, right? Uh and and I I think

problems, right? Uh and and I I think that felt different from 24 maybe where there was a lot of energy focused on just pre-training scale and LLM.

>> Yeah. And several several other dynamics. Um, as the market grows, each

dynamics. Um, as the market grows, each one of these models could choose to have verticals >> or segments where they want to differentiate.

>> Somebody could decide to be a better coder. Somebody could decide to be just

coder. Somebody could decide to be just better at being easier to be accessible so that it could be a greater consumer product.

>> You know, the diversity of these models.

As a result, you could you could probably make a niche leap without having to be great at everything else and still be super valuable to the market.

>> It's no longer necessary to boil the entire ocean. The f two years ago,

entire ocean. The f two years ago, because it was called pre-training pre, you know, people people said, well, you know, pre-training is over. First of

all, pre-training is not over. But the

point of pre-training is to train yourself for training. That's why it's called pre-training to prepare yourself to do the real training. And now we call it post-training. It's kind of weird. I

it post-training. It's kind of weird. I

I think it's just training, but pre-training is pre-training and therefore it's training. Training as you as as we all know is is where uh compute

scaling directly translates to intelligence. You you've you've largely

intelligence. You you've you've largely now now this the the data the the data necessary to train a model is actually pretty small. Maybe it's just the

pretty small. Maybe it's just the verifiable results. Now it's really

verifiable results. Now it's really algorithmic, very compute intensive and so and you don't have to be good at everything in life as you know just like all of us we don't we could decide

because we don't have time to learn everything equally well. We decide to choose a specialty and focus all of our energy on it and we become superhuman or incredibly good at something that other

people are not. And so I think AI labs are going to start doing the same.

They're going to start bifurcating into various segments and over time you're gonna and startups will do the same.

>> They'll find a micro niche and they'll take something open and then be incredibly good at it.

>> Well, I think one of the most optimistic views here is uh actually that these microniches are quite valuable, right? I

was talking to Andre um because I've been talking to a lot of people about their predictions for next year. We'll

ask you yours as well of course. Um um

but he asked you know what is a what's an example of a prediction that would have been preient last year uh and my answer everything's easy in retrospect is that coding would be the first

application level business that gets to a billion of AR as an AI native app right and I I think if you taken an old world view of this >> um you would have believed like one of

two narratives right one is uh single model does everything and it'll all just be subsumed into something monolithic Mhm.

>> And two is that developer tools never get very big, right? Well, kind of depends on how valuable the developer tool is. Now, I think many more people

tool is. Now, I think many more people understand software engineering is in a niche and there's more demand than ever for it, >> but I think we'll see more like that.

>> Also interesting, uh we are using we we use cursor here and we use cursor pervasively here. Every engineer uses it

pervasively here. Every engineer uses it and the number of engineers, you just mentioned it, the number of people we're hiring today is just incredible.

>> Yep.

>> Right. Monday is come to work at Nvidia day and and um uh why is that? Uh this

is now the purpose and the task.

>> The purpose of a software engineer is to solve known problems and to find new problems to solve.

Coding is one of the tasks.

>> And so if the purpose is not coding, if your purpose literally is coding, somebody tells you what to do, you code it. All right? Maybe you're going to get

it. All right? Maybe you're going to get replaced by the AI. But most of our software engineers, all of our software, their goal is to solve problems. And it turns out we have so many problems in the company and we have so many

undiscovered problems. And so the more time they have to go explore undiscovered problems, the better off we are as a company. Nothing would give me more joy than if none of them are coding at all. They're just solving problems.

at all. They're just solving problems. >> You see what I'm saying? And so I I think that this framework of purpose versus task is really good for everybody to apply. For example, somebody who's a

to apply. For example, somebody who's a waiter, their job is to not to take the order. That's not their job. As it turns

order. That's not their job. As it turns out, their job is so that we have a great experience. And if somebody if

great experience. And if somebody if some AI is taking the order, their job or even delivering the food, their job is still helping us have a great experience. They they would reshape

experience. They they would reshape their jobs accordingly. And so so I think the um the question about about cost of compute um uh is really

important. Let's let let me come back to

important. Let's let let me come back to one the the reason why we are so dedicated to a programmable architecture versus a fixed architect. Remember a

long time ago >> uh a CNN chip came along and they said Nvidia is done.

>> And then and then a transformer chip came and Nvidia was done.

>> People are still trying that. Yes.

>> Yeah. NP and and the benefit of these dedicated AS6 of course it could perform a job really really well and transformers is a much more universal AI

network but the transformer as you know the species of it is growing incredibly >> the attention mechanism >> the attention mechanism how it thinks about context

diffusion versus auto reggressive >> these hybrid SSM transformation >> hybrid SSM for example Neotron we just announced a new hybrid SM SM and and so

the architecture of transformer is in fact changing very rapidly and over the next several years it's likely to change tremendously and so we we dedicate ourselves to an architecture that's flexible for this reason so that we can

on the one hand adapt with remember because MOS law is largely over transistor benefit is only tens 10% maybe a couple of years

>> and yet we would like to have hundreds of X every year and so the benefit is actually all in algorithms and an architecture that enables any algorithm is likely going to be the best one right

because the transistor didn't it didn't advance that much and so I I think the the our dedication to programmability is number one for that reason we have so much optimism for innovation and

algorithms and iteration software that we protect our programmability for that reason the second thing is is by protecting this architecture

our installed base is really large. When

a software engineer wants to optimize their algorithm, they want to make sure that it doesn't run on just one this one little cloud or this one little stack.

They want it to run on as many mo on as many computers as possible. So the the fact that we protect our architecture compatibility then flash attention runs everywhere. So SSM run everywhere,

everywhere. So SSM run everywhere, diffusion runs everywhere, auto reggression runs everywhere. Just

depending it doesn't matter what you want to do. CNN still run everywhere.

LSTM still runs everywhere. And so that this this architecture that is architecturally compatible so that we have a large installed base programmable for the future is really important in

the way that we help to advance and as a result all of this drives the cost down [clears throat] and and I'm super proud that that um uh our latest innovation

MVLink72 we're the lowest cost token generation machine in the world by enormous amounts and the reason for that is because are really really hard

>> and so you know people didn't expect that um that forees it's probably easier to train but for inference is incredibly hard to generate tokens on but as as cost drop usually you open up new

applications or new verticals that become more and more accessible >> and we talked a little bit about coding like cursor and cognition and other companies that are really benefiting from that in this last year do you have any thoughts or predictions in terms of

what the next breakthrough industries will be or new applications or areas that you're most excited about coming in 26 in particular like Are there one or two things that you think will >> because of three things I because of

because of a couple two three things I I think I think several industries are going to are going to experience their chat moment. Um I believe that

chat moment. Um I believe that multi-modality and um very long context is going to enable of course really really cool chat

bots. Um but the basic architecture that

bots. Um but the basic architecture that in combination with breakthroughs in synthetic data generation is going to help create the chat GPT moment for

digital biology.

>> That moment is coming.

>> And by digital biology, do you specifically mean other aspects of like protein folding or protein binding or protein diagnosis? I see proteins.

protein diagnosis? I see proteins.

>> I think we're good at protein understanding. Mhm. Now multi-proin

understanding. Mhm. Now multi-proin

understanding is coming online and we recently created a model called LA prina. It's open. Um it's for

prina. It's open. Um it's for multi-proin >> um understanding and and represent representation learning and generation.

Uh so so I think that the protein understanding is is advancing very quickly. Now protein generation is going

quickly. Now protein generation is going to advance very quickly. Chat GPD moment proteins.

>> Yeah. There are a lot of interesting companies working on molecule design in endtoend way like chai.

>> Exactly. And then and then of course chemical understanding and chemical generation and then protein chemical >> confirmation understanding and generation. Is that right? And so that

generation. Is that right? And so that combination the chat GBT moment the generative AI moment all of that stuff is coming together for for um digital biology >> and to your to your point about like new

industries or you know the way I think about it is like investing in the inputs for this AI as well. All of these things around biology and chemistry and material science, they require real world data generation and

experimentation, right? And that's new

experimentation, right? And that's new infrastructure too.

>> New infrastructure, uh, synthetic data is going to be really important because they just have such sparse, right? Spar

sparity of data and they just don't have as much as human language. And there the the real breakthrough is going to be when we can train a a world foundation

model, a foundation model for proteins, a foundation model for cells. I'm I'm

very excited about both of those things.

Once we have a a foundation model, our understanding capability, our generative capability, that data flywheel is really going to take off.

>> The this this the second area that I'm excited about, um, of course, reasoning made huge breakthroughs in language, but because of reasoning, cars are going to be able to perform better. So, instead

of just perception cars and planning cars, they're going to be reasoning cars. So, these cars are going to be

cars. So, these cars are going to be thinking all the time. And when they come up they come up to a circumstance they they've never en encountered before they can break it down into circumstances they have encountered it

before and construct a reason reasoning system for how to navigate through it.

And so the out of domain out of you know out of distribution >> part of AI is going to very much be be addressed by reasoning systems or and as

a result we could do more things than we were taught to do between uh generative AI uh and um multimodal uh you know vision language action models and

reasoning systems. I think we're going to see big breakthroughs in human robots or multi-mbodiment robots. you know does >> what do you think what do you think is a time frame for that because if you look at the self-driving analog and obviously

self-driving technologies were based on very different types of neural networks than what we're using today in terms of you know there's been a big swap over the last two three years >> in terms of how we do a lot there >> we started too soon

>> self-driving cars really had four eras era was smart sensors >> connected into a car >> the mobile [clears throat] eye era >> the mobile eye era and even even the

very earliest days of of Yeah.

>> Yeah. Even the earliest days of Whimo, >> the the um you're talk you're using smart sensors um a lot of human engineered algorithms >> and education severe mapping as far

>> extreme mapping >> mapping and then different systems for planning and perception.

>> Exactly. And so so you're essentially creating a car that is driving on digital rails, right? It's no different than than the rails at Disneyland. There

are digital rails. And so that's the first generation. the second generation.

first generation. the second generation.

Um and during that generation you have perception, world model and planning.

>> Mh.

>> And and the these modules um and each one of these modules have the limits of their technology and and perception was first imple was was first affected by deep learning uh first and then and then

uh and then it propagated through the pipeline.

>> And so that but that system was too brittle >> and it only knows how to perform what you taught it. And now where we are are endtoend models and then and then where

we're going to go next are end to end models. There you go. So that those are

models. There you go. So that those are kind of the four eras in a lot of ways.

If we would have started self-driving cars probably three years ago, >> we would probably be exactly the same place.

>> All our poor friends who were working in self-driving. Yeah.

self-driving. Yeah.

>> And and I don't I don't mind it. I've

been working on on it for 10 years.

Nvidia's self-driving car stack, by the way, number one rated safety in the world today.

>> Number one, we just got we just got that rating today uh last week. And number

two is Tesla. So, I'm very proud that two American companies are up on the >> Are you um So, from a robotics perspective, you think because we've already built all these sorts of technologies in the modern era, robotics

won't have the same 10, 15 years. That's

right.

>> I'm much more optimistic with robotics because we we've kind of >> advanced foundational technology.

>> Now, you know, people are thinking about human robotics. Human robotics has a lot

human robotics. Human robotics has a lot of challenges. I mean, there's all the

of challenges. I mean, there's all the megatronics challenges there. You know,

like for example, >> it's not helpful if the robot weighs 300 lb >> and what happens if it falls over and interacting with kids and so on so forth. And so, so you got all kinds of

forth. And so, so you got all kinds of challenges to deal with. I'm certain

that we're going to we're going to solve those. But remember the fundamental

those. But remember the fundamental technology that goes into a human robot robot can go into a pick and place robot.

>> Um it could be it could be um how do you think about one thing I've been curious about for robotics in particular is if I look at who won or who who who's perceived as winning in self-driving.

>> It's largely incumbents, right? It's

Whimo, it's Tesla. You mentioned uh the safety rating Nvidia's gotten. And so

it's people who've been working on this for a long time. It took a lot of capital. It was really intensive to get

capital. It was really intensive to get there. You have supply chain, you have

there. You have supply chain, you have hardware, you have all this extra complexity. Do you think the same thing

complexity. Do you think the same thing will be true in robotics? Are the

winners basically going to be Tesla with Optimus and other people who have both been in the industry for a while but also have all those sort of incumbent effects? Do you think there's room for

effects? Do you think there's room for startups?

>> They will be one of the leader one of the one of them and and and surely a major one. Um but everything

that moves will be robotic.

>> Everything that moves will be robotic.

And everything that moves is a very large space. It's not all human or

large space. It's not all human or robot. And yet every AI will be

robot. And yet every AI will be multi-mbodiment meaning you know just like just like a human with our m our multi-mbodiment AI ourselves

>> we could sit in a car >> and embody that >> we could pick up a tennis racket embody that we could pick up a chopstick embody that >> and so we could embody the >> people are general purpose right they can do all these things

>> exactly and so AIS are going to become general purpose so you have one arm pick and place maybe it's two arms pick and place could be six arms pick and place, you know. So, so I think you're going to

you know. So, so I think you're going to have all kinds of different sizes and shapes. It could be a caterpillar. It

shapes. It could be a caterpillar. It

could be, you know, it could be an excavator. It could be all kinds of

excavator. It could be all kinds of stuff. And so AI will embody those just

stuff. And so AI will embody those just as a just as a a construction worker embodies an excavator embodies a tractor. You know, they you know,

tractor. You know, they you know, >> could there be a small number of companies then that do the embodiment for everything or are you saying more there's going to be niche applications?

You should definitely see a lot of software companies and then those that software company could serve a lot of a lot of different >> verticals but each one of the verticals will still have solution providers that

then grounds it all turns it into something that works perfectly. Does it

make sense? Because in the case of AI for consumers if it works 90% of the time you're delighted you you're you know you're mind blown. If it works 80% of the time you're satisfied. In the

case of most industrial and physical AIs, if it works 90% of the time, nobody cares about that. They only care about the 10% that it fails. Basically, you

know, 100% dissatisfaction. And so, you got to take it to 99.99999.

So, the core technology might be able to get get you to 99%.

>> And then a vertical solution provider like a Caterpillar or somebody, they could take that core technology and make it 99.999% great. Do you think that's what happens

great. Do you think that's what happens like earliest on because in in markets that are this immature it seems one of the fastest paths to market could be full verticalization right because you just have control of iteration speed

>> the different the the difficulty difficulty of of verticalization for technology that that is general purpose is that you don't have the R&D scale to build a general purpose technology. Now,

of course, open source helps that tremendously, >> which is the reason why you're going to see a, you know, a a big surge of vertical opportunities in AI in the next several years.

>> My my prediction would be over the course of the next five years, the excitement is going to be verticalization.

>> Notice we we're excited about Open Evidence, we're excited about Harvey, we're excited about Cursor. cursor is is a horizontal but it's kind of a horizontal vertical >> you know and so um I'm I'm super excited

about all the verticals >> you know a lot of people said yeah AI is gonna get so god AI is going to get so good that all these rapper companies are going to be obsolete it's just it misses the big point

>> you know the reason why you could talk about the reason why somebody can talk talk about somebody is creating technology could talk about the life of a surgeon is because they've never been a surgeon the reason why somebody who

builds at AI and talk talks about the life of a accountant and a tax, you know, a tax expert because they've never been a tax expert, you know, and so so I I think they just the reason why somebody could talk about being a bus

boy without being a bus boy is they never been a bus boy. And so so I I think you you you've got to be a little bit more empathetic about the depth of the complexity of the work >> and and tr try to truly understand the

purpose of the work. Often times the the technology addresses the task, it doesn't address the purpose. So I guess one of the other narratives from we're looking at narratives that are true

versus not true, you know, for 25. One

other narrative that's come up has been more about energy and energy utilization and will we have enough energy to support AI. How do how do you think

support AI. How do how do you think about that? On the first week of

about that? On the first week of President Trump's administration, he said drill, baby drill. He got so much flack for that.

If not for this entire change in in sentiment about energy growth in our country, >> we can all concede now we would have

handed this industrial revolution to somebody else.

>> And we're still power constrained.

>> We're still power constrained. Yeah.

>> Without energy, there can be no new industry.

>> Mhm. And of course, we've been energy starved now for what, a decade. If not

for the fact that President Trump reversed that narrative, we would be completely screwed.

>> Mhm.

>> Without energy, you can't have industrial growth. Without industrial

industrial growth. Without industrial growth, the the nation can't be more prosperous. Without being more

prosperous. Without being more prosperous, we can't take care of domestic issues. We can't take care of

domestic issues. We can't take care of social issues. You know, on and on and

social issues. You know, on and on and on. And so, the fact of the matter is,

on. And so, the fact of the matter is, we need energy to grow. We need every form of energy. We need, you know, natural gas. We need to be, of course,

natural gas. We need to be, of course, we need more energy on the grid. We need

more energy behind the meter. Uh we're

going to need nuclear. Uh wind is not going to be enough. Solar is not going to be enough. Let's just all acknowledge that we'll take it. We'll take

everything we can. Um but the fact that matters, I think, for the for the next decade, >> natural gas, you know, is probably the the only way to go forward. What's

really interesting is I I agree the timeline is too far out to address people's um you know power generation issues in 27 and 28 where uh you know

large players building clusters are very concerned but the the biggest drivers of like climate innovation in the US have actually been as a result of this AI

infrastructure problem right because people look at the demand >> finally that's right demand >> they look at the demand and the demand is driving people to create massive of new battery companies, solar

concentrators. It's put new energy be

concentrators. It's put new energy be new energy like you know willpower behind >> SM the AI industry is driving all of that sustainable energy industry.

>> Yeah.

>> Um because people see that there is going to be demand for it right so even if and I think there is no practical answer in the small number of years time

frame versus uh large gas right um uh it still drives climate innovation. Yeah,

no question about it. No question about it. And I I think that's exactly right

it. And I I think that's exactly right that that you know doomer messages um causes policy and that policy may may affect the industry in some way. But

there's nothing more powerful than demand. Look at all the jobs that's

demand. Look at all the jobs that's being created. Look at all the the

being created. Look at all the the industries that's being formed around it. um sustainable energy likely and

it. um sustainable energy likely and when history rewrites it as Sarah, I think you you're going to be absolutely right that that if not for AI, well AI was is probably the biggest driver for

sustainable energy ever.

>> Yeah. A friend of mine has a saying that uh doomers are the people who sound smart at dinner parties and optimists are the people who drive humanity forward. And I think that's very true

forward. And I think that's very true for for all these things we've talked about. Yeah. So

about. Yeah. So

>> yeah, it's really true.

>> Yeah. Well, that that's one of the big big um takeaways for for uh this last year, the battle of narratives.

>> And it's too simplistic um to say that everything that the doomers are saying are irrelevant.

That's not true. A lot of very sensible things are being said. Um it is too simplistic to say that when somebody is optimistic that they're just naive.

>> It needs to be grounded in reality.

Yeah, that optimistic people are just naive, you know, >> and that that's obviously not true.

>> Um, but I think we just have to be mindful of the balance of it.

>> When 90% of the messaging is all around the end of the world and doom and the pessimism and you know, I think we we're scaring people >> from making the investments in AI that

makes it safer, more functional, more productive >> and more useful to society. And so we just, you know, more secure. We, you

know, all of that takes technology.

Security takes technology. Safety takes

technology. I appreciate that my car is safer today because it has better technology than a car 50 years ago.

>> And so so I I think it takes technology to be safe, technology to be secure. And

so I I'm I'm I'm delighted to see that the the advancement of technology is still accelerating and ongoing. And so

we just have to make sure that the the policy makers around the world, the governments um are able to are are thinking about balancing these two ideas.

>> How do you So I guess we've talked a lot about 25 >> and the narratives of 25. How do you think about 26? What are you excited about? What do you see coming? What do

about? What do you see coming? What do

you think are big changes that we should be aware of?

>> I am optimistic that that um our relationship with China will improve.

Mhm. [clears throat]

>> that President Trump and the administration um has a really really grounded and common sense um attitude about um and philosophy around around

how to think about China that that they're an adversary >> um but they're also also a partner in many ways and that the idea of

decoupling is naive and the idea of decoupling um for whatever reason philosophical reasons or national security reasons It's just not not it's

not based on any common sense and the more you the more deeply you look into it the more the two countries are actually highly coupled.

>> Um both countries ought to ought to invest in their own independence. Um I

you know when you depend too much on someone the relationship becomes too emotional uh as you know [laughter] and so it's good to have some independence or as much independence as either either

would like but to recognize that there's a lot of coupling a lot of dependence between the two countries and and I think there's a there needs to be a nuanced strategy a nuanced attitude

about how to how to how to manage this relationship in a productive way for all of the people of two countries and for all of the people around the world, everybody depends on a productive,

constructive relationship of the two most important nations and the single most important relationship for the next century. And so we have to find that

century. And so we have to find that answer. And I'm I'm I I'm just really

answer. And I'm I'm I I'm just really delighted uh that President Trump is looking for a constructive answer. And

so I I think that next year uh will be a much better better better year than the last several. I'm happy with the

last several. I'm happy with the administration was able to to to suggest a a an export control um policy that is

grounded on national security recognizing that they already make so many chips themselves and they they can depend on Huawei themselves for their military for their national security.

they got ample technology to do that.

And so that American technology, although general purpose um is unlikely to be used by their military because their military is too smart, just as our military is too smart to to use their

technology. And so it's grounded on

technology. And so it's grounded on national security. It's grounded on on

national security. It's grounded on on uh technology leadership. It's grounded

on national prosperity. You know, one of the things that that we just always have to remember is that the world's mightiest military uh is supported by the world's mightiest mil economy. And

so the wealth that we generate um brings jobs home, creates prosperity in the United States, um provides for tax revenues, and ultimately funds the mightiest military on the planet. And so

that circular system, that interconnected system requires a nuanced strategy. and and um uh and and and and

strategy. and and um uh and and and and I'm I'm I'm pleased to to to to see some of the progress in that area that allows American technology companies to keep

America first and keep America ahead >> and to to support American technology leadership on the one hand um to win globally >> and and then and then China of course is

sorting itself out you know I mean not sorting but they're sorting out the attitude about how to think about American technology and there >> because historical argument there has been that if if you look for example at the internet um there was what was known

as a great firewall right China basically >> prevented US competition into China while the opposite wasn't as true >> um there's been mass expatriation of US jobs and industry to China as sort of

part of the development of the 90s and 2000s and so I think a lot of the things that people have brought up from a China US policy perspective besides just the military adversarial relationship um or spheres of influence or you know all the

various things like that is also just the economic imbalances that have perceived to exist between the two countries. The way that I would think

countries. The way that I would think through that is go back to the first principles of technologies again >> and and let's say the internet you have the chip industry you have the systems

industry the software industry you have the services industry on top remember China's internet growth has been a boon for Intel and AMD selling CPUs

>> Micron selling DRAM skinex and Samsung selling DRAM >> it is the second largest internet market for American technology industry

>> and so so maybe maybe it wasn't helpful to some layer of the stack >> the Googles of the world >> but don't exclude every layer of the stack always come back every single one of these things take a step back and

look at the whole stack >> maybe that's a theme for today as well and it makes sense that you would you would send this message but you know technology is actually not just the the sort of internet software application

layer that's been very dominant for two decades >> it's the whole stack and Remember as as as Intel and AMD prospered >> uh with the internet industry uh in

China growth the China industry growth don't forget China also contributed tremendously to open source. No country

in the world contributes more to open source than China. And look at all the startups here in America that were able to benefit from that open source to create the the new startups that are

here. And so you can't look at one area

here. And so you can't look at one area in isolation. You have to look at the

in isolation. You have to look at the whole life cycle of the technology and look at every layer of the stack. Does

it make sense? When you take a look at that from that lens, >> China's internet industry generated enormous prosperity for America.

>> Mhm.

>> Just not at the internet company per se.

>> Jensen, my other investor friends will not forgive me if I don't ask you about 2026 um uh on the business side. Uh are

we in an AI bubble? AI bubble. Yeah,

there's a lot of ways to reason through that.

>> And so, so again, um, you know, when when asked that question, my mind goes to what is AI and where are we in that?

There's AI, then there's computing. You know, as you know, Nvidia invented accelerated computing. Accelerated computing does

computing. Accelerated computing does computer graphics and rendering. AI

doesn't. Um, accelerated computing does data processing, SQL data processing. AI

doesn't.

>> Um, accelerated computing does molecular dynamics and quantum chemistry. AI

doesn't. You know, all these are all things that people could say someday AI will, but it doesn't today. Accelerated

computing is really essential for uh classical machine learning, XG boost, recommener systems, the whole process of uh feature engineering, extract, load,

and transform. That entire data science,

and transform. That entire data science, machine learning life cycle, accelerated computing is used for all of that. The

first thing to go to is in the context of Nvidia.

What we see is the the the dynamic is the shift from general purpose computing to accelerated computing because MOS laws largely ended. You can't use CPUs

for everything anymore like you used to.

And so it's just no longer productive enough. It's not deflationary enough.

enough. It's not deflationary enough.

>> And so so we have to move towards a new computing model. And that's where

computing model. And that's where accelerator comes in. If you if generative AI well excuse me if chatbots let's just go you know open AI and Anthropic and Gemini if none of that

existed today Nvidia would be a multiundred billion dollar company and the reason for that is because as you know the foundation of computing is shifting to accelerated computing

>> that's the first thing to to realize is is to take a step back and ask yourself what is actually happening now the next layer up the question about AI now

becomes What is AI? Now, we ask that we ask the AI bubble question and we always go back to OpenAI's revenues 100%. Don't

we?

>> Mhm.

>> You ask somebody, hey, is there an AI bubble? Everybody goes directly to

bubble? Everybody goes directly to OpenAI's revenues. First of all, if

OpenAI's revenues. First of all, if OpenAI currently has twice the capacity, their revenues would double. You guys

know that if they have 10 times the capacity, their I really believe their revenues would 10 times. And so, they need capacity. This is no different than

need capacity. This is no different than Nvidia needs wafers from TSMC. Just

because you know Nvidia exists and and we're doing great doesn't mean we don't need capacity. We need capacity. We need

need capacity. We need capacity. We need

capacity of DRAM. We need and so in our world it's sensible to everybody. We

need capacity. Well, in their world they need factories >> and if they don't have factory capacity how they generate tokens, which is where we started our conversation today and so they need factory capacity in order to

increase their revenue growth. But

nonetheless, we also said that AI is more than chatbots. It includes all these different fields of science. Um,

Nvidia's AV business is coming up on 10 billion dollars. Nobody ever talks about

billion dollars. Nobody ever talks about that. And you have to train world

that. And you have to train world models. You have to train these AI AVs

models. You have to train these AI AVs and it's happening robo taxis happening all over the world. Our AI work with uh digital biology, our AI work in financial services. The whole industry

financial services. The whole industry of quants, quantitative trading is moving towards Yeah, exactly. They used

to be classical machine learning. A

whole bunch of human featured they call quants, right? These these specialized

quants, right? These these specialized mathematicians were trying to figure out what the predictive features are. Now we

use AI to figure it out. And so in order to have instead of having quants, you need a lot of supercomputers. Financial

services is one of our fastest growing segments. billions of dollars in in

segments. billions of dollars in in quants, you know, in financial services, billions of dollars in AV, billions of dollars in robotics coming up, billions

of dollars in digital biology. And so

how big can that all that be? Well,

simple logic is this simple math.

Whether you you think that AI is going to replace shortage, labor shortage or workforce shortage in any kind, um, let's ignore that for a second. The

world is at hundred trillion dollars in GDP. out of that let's just say 2% 2%

GDP. out of that let's just say 2% 2% annually is R&D and let's just go back in time five years ago if you were to take the largest drug discovery company

in the world drug company in the world and where's all of their R&D wet labs >> today what are they do doing building supercomputers

>> and so there's a fundamental shift in how they think about that $2 trillion >> it used to be $2 trillion for the old way of doing things. It's now going to be $2 trillion in the AI way of doing

things. Well, $2 trillion is going to

things. Well, $2 trillion is going to need $2 trillion of R&D is going to be powered by a whole bunch of infrastructure. And that's the reason

infrastructure. And that's the reason why we're building supercomputers everywhere around the world. And so so I think if if you reason about it from the outside in, you know, either from the

foundation up, from the outside in, you come to the conclusion that what we're experiencing, what all three of us are experiencing, which is the amount of computing demand is insane.

>> Give me an example of a startup company that goes, "No, we're good."

>> They are all dying for computing capacity. Give me an example for a

capacity. Give me an example for a researcher in any university, a scientist in any company who says got plenty of capacity. Everybody is dying

for capacity. And so we have a global

for capacity. And so we have a global multi- company, multi-industry shortage.

It's not just about open AI even though open AI could use a lot more capacity as well. So I think I think how we think

well. So I think I think how we think about this what with the narrative the narrative is not helpful and it's a little bit too superficial to say how do

you prove there's an AI bubble$12 billion of revenues hundreds of billions of dollar infrastructure being built is a little bit too simplistic.

>> Yeah. The other thing people um tend to point out is the MIT study. There

there's some study that I think came out of MIT that claimed that most enterprise deployments of AI weren't that useful.

And you're like, well, did you do the change management? Did you do a reorg?

change management? Did you do a reorg?

Did you integrate into tooling? Did you

like how long did it even take to implement it? If a planning cycle in an

implement it? If a planning cycle in an enterprise is a year and is something in six months and so it feels like there's a lot of these kind of again overstated things that get a lot of attention, but then you map it against what's actually happening.

>> Yeah.

>> And the growth of these companies using AI and it's just a completely different world. And and and if you want to find

world. And and and if you want to find out where the world's innovation's happening, I would not go find out at an enterprise.

>> Would you guys agree?

>> Yeah.

>> Enterprise is like the slowest adopters of new technologies. I would go talk to all of the startups, the 30, 40,000 startups that are currently doing this stuff. I would go talk to Open Evans.

stuff. I would go talk to Open Evans.

How how's it working? I would go go talk to cursor. How's coding working by the

to cursor. How's coding working by the way? You know, I would just go talk to

way? You know, I would just go talk to these people.

>> I think it's really interesting that you see that. Um, of course you do have

see that. Um, of course you do have companies making, you know, hundred million plus, multiund million plus progress of AR in enterprise sales, Harvey, Sierra, etc. But some of the

fastest growing companies have been enduser adopted even in conservative industries, right? Like healthcare, you

industries, right? Like healthcare, you know, skeptical industries like engineering, >> healthare, the most right, the most conservative of all. But guess what?

They are so concerned about getting the right answer >> that the ability to have something like open evidence.

>> Yeah.

>> To do grounded research, high quality research and get that get that research as information to you. Nobody wants to do research. They want answers. Nobody

do research. They want answers. Nobody

wants to do search. They want answers.

Is that right?

>> A bridge is a great example of that too where they're basically making it really easy to do the physician knows instead of the physician sitting there and doing it. Back to your point on task versus

it. Back to your point on task versus >> task versus purpose. Exactly. And I

think a different way to think about the demand is like there are so many jobs where you're asking the the work is actually like an impossible ask right of a doctor or a radiologist keep up with

the world's biomedical knowledge in R&D which is accelerating you know computing and otherwise um and then >> like archive papers >> there was a time you s you and I read

>> you and I both both used to do I don't do that anymore but here now now I just load it all into chat >> GBTh you Now I just load it all in with all of the the ones that are interesting

and and I make it learn it >> and then I you know make it summarize and another summary and I I interact with it. But but the point is uh we used

with it. But but the point is uh we used to do search. We don't do it that anymore. I don't do search. We used to

anymore. I don't do search. We used to do research. You know the goal is to get

do research. You know the goal is to get answers. The goal is to get smarter. And

answers. The goal is to get smarter. And

these AIs allow us to help us do all that. And I think all of it all of it

that. And I think all of it all of it comes back with it. It's all more helpful if you come back to the framework that says AI is a multi-layer

cake >> and that AI is not just a chatbot. AI is

very very diverse in all of the industries and modalities and information and applications that it addresses. When you think about wanting

addresses. When you think about wanting to win >> that America should win AI, it should not just be America should have this company win AI, but it we should try to

win across the board >> and across domains.

>> Across domains. Exactly. And when we think about open source, all of a sudden this this is a helpful framework. When

we think about winning, it's a helpful framework. When we think about uh energy

framework. When we think about uh energy is a helpful framework that because we need factories. Factories need energy.

need factories. Factories need energy.

And without energy, we have no factory.

without factories we have no AI that's a helpful framework and so I think if if um if we if we have a better understanding a system a framework for

understanding what AI is I think the narratives will become more common sense the narratives will become more pragmatic >> become more balanced we want to keep people safe

>> but one of the best ways to keep people safe is advancing advancing of technology quickly >> and and I think the industry is doing that and I'm very proud of the industry for doing that.

>> No one wants to drive a car from, you know, the first decade of cars. And so I I think uh >> ABS is a really good thing.

>> Yes, >> ABS is a really good thing. Lane keeping

is a really good thing. There's no

question FSD is a really good thing.

>> And I think people will be excited about the, you know, third or fourth year of AI.

>> Yeah. No, no doubt. And and I I say with great pride that the industry made tremendous strides this last year.

all the technologies we've mentioned.

Um, and that the scaling laws are so intact that we we now know that more compute, more intelligence

>> and and um uh gosh, the the the the innovations in one in in one sector diffuses and spreads across all of the other sectors so fast. I'm so happy to

see all that. And so I think the next five years it's going to be extraordinary. No, no doubt about it.

extraordinary. No, no doubt about it.

And I think next year is going to be incredible.

>> Amazing. Well, we're excited to talk to you at the end of next year, too.

>> Yeah. Looking forward to it. Thank you

guys for all the work that you guys do.

Congratulations. What a great year.

>> Wow. Amazing year.

>> Yeah. A lot. Thank you.

>> Yeah. Thank you. Happy New Year. Happy

New Year.

>> Find us on Twitter at No Prior Pod.

Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever [music] you listen. That way you get a new episode every week. And sign up for

emails or find transcripts for every episode at no-briers.com.