No PriorsNVIDIA’s Jensen Huang on Reasoning Models, Robotics, and Refuting the “AI Bubble” Narrative
EVERY SPOKEN WORD
75 min read · 14,674 words- 0:00 – 0:17
Jensen Huang Introduction
- EGElad Gil
[upbeat music] Benson, thanks so much for joining us today.
- JHJensen Huang
So great to have you guys.
- EGElad Gil
Yeah.
- JHJensen Huang
What an amazing year!
- EGElad Gil
What a year. Things just happen.
- JHJensen Huang
Happy Hanukkah. Merry Christmas.
- SPSpeaker
Happy holidays.
- JHJensen Huang
Happy New Year coming up. Yep, happy holidays.
- EGElad Gil
Yeah.
- SPSpeaker
So,
- 0:17 – 4:12
Biggest AI Surprises of 2025
- SPSpeaker
uh, with everything that's happened in twenty twenty-five, 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?
- JHJensen Huang
Let's see. There, there are 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 of grounding. I was pleased with the improvements of reasoning. I was pleased with im- 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.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
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 and every field, from language to vision, to robotics, to self-driving cars, the up- the application of reasoning and the grounding of the, of, of, of, of the answers, um, big, big leaps. Would you guys say this year?
- EGElad Gil
Oh, huge. Yeah. I mean, things like open evidence too, for medical information, where doctors are now really using that as a trusted resource. Like you, uh, Harvey, for legal, you're, 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.
- JHJensen Huang
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, 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 time, it seems. [chuckles] And, uh, and I'm so pleased that, that these tokens are now profitable, that people are generating... I heard somebody, uh, or heard, heard today that, that open evidence, speaking of them-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... Ninety percent gross margins.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
I mean, those are very profitable tokens.
- EGElad Gil
Yeah.
- JHJensen Huang
And so they're obviously doing very profitable work, very valuable work. Cursor, their margins are great. Uh, Claude's margins are great.
- EGElad Gil
Mm.
- JHJensen Huang
For the enterprise use 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 it. And so I, I think these are, are really great grounding for 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 hi- all of my life combined. You know, [chuckles] my average elevation this year is probably about seventeen thousand feet-
- EGElad Gil
Half
- JHJensen Huang
... you know? [chuckles]
- EGElad Gil
Yeah.
- JHJensen Huang
So it's nice to be here on the ground with you 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, 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-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... um, uh, labor shortage. I mean, boy, we covered everything-
- EGElad Gil
It's a lot
- JHJensen Huang
... did we not?
- EGElad Gil
It's a lot, yeah. I remember-
- SPSpeaker
Everything was AI.
- EGElad Gil
Yeah.
- JHJensen Huang
Everything was AI.
- EGElad Gil
Yeah.
- JHJensen Huang
Yeah, it was incredible.
- EGElad Gil
Yeah, AI was definitely the center of the storm for, like, every one of those themes.
- 4:12 – 9:03
AI and Jobs: New Infrastructure and Demand for Skilled Labor
- EGElad Gil
Maybe when we can start with actually, um, is jobs, because- or jobs and employment.
- JHJensen Huang
Mm.
- EGElad Gil
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?
- JHJensen Huang
Mm-hmm.
- EGElad Gil
The people who were most trying to push the field forward were often the people who are most pessimistic, which is very odd.
- JHJensen Huang
Mm-hmm, mm-hmm.
- EGElad Gil
Why would you do both at once? And I feel like that narrative has taken over some subset of media or some subset of other things, despite all the things that we think are very positive about what AI has done-
- JHJensen Huang
Mm-hmm
- EGElad Gil
... how it's gonna help with healthcare, with education, with productivity, with all these other areas.
- JHJensen Huang
Mm.
- EGElad Gil
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.
- JHJensen Huang
That's right.
- EGElad Gil
Could you talk about how you think about employment and jobs and sort of-
- JHJensen Huang
Mm-hmm
- EGElad Gil
... what people are saying and what you think the real narrative is there?
- JHJensen Huang
Maybe what I'll do is I'll, I'll ground it on, uh, three points in space, three points in time. Now-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... uh, maybe, uh, a very near future, and then some, some point out, out in the distance, and, and maybe, maybe some counter-narratives.
- EGElad Gil
Mm.
- JHJensen Huang
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-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... but it's not prerecorded software, as you know. For example, Excel was written by several hundred engineers. They compiled it, it's prerecorded, and then they distribute it as is for several 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 generates every single token for the first time, every time.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
Which means every time you use the software, in, in everything that we do, AI is being generated for the first time 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, uh, quite unique about AI is that it needs these computers to generate these tokens every 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.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
It's used in every single company. It's used in every single industry. It's used in every single country, therefore, it's part infrastructure, like energy and, and 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 new type of plants have to be created. Number one, we have to build a lot more chip plants.
- SGSarah Guo
Mm-hmm. Mm-hmm.
- JHJensen Huang
TSMC is building, right? SK Hynix is building a lot more plants, and so we need more chip plants. We need more computer plants.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
These computers are very different. These are supercomputers that the world's never seen before, right? Grace Blackwell, uh, looks like a very different type of computer than anything that's ever been made, and entire rack is one GPU.
- 9:03 – 12:31
Task vs. Purpose Framework in Labor
- JHJensen Huang
And this is where the difference between task versus purpose of a job.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
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.
- SGSarah Guo
Mm-hmm, and to do research.
- JHJensen Huang
And, and that- exactly, and to do new 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, what is the purpose of the job-
- SGSarah Guo
Mm
- JHJensen Huang
... versus what is the task that you do in your job? And, and as you know, I spend most of my day typing. That's my task-
- SGSarah Guo
Uh-huh
- JHJensen Huang
... but my purpose is obviously not typing.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
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-
- SGSarah Guo
Mm-hmm
- JHJensen Huang
... um, it hasn't really made me, if you will, less busy.
- SGSarah Guo
Mm.
- JHJensen Huang
In a lot of ways, I've become more busy because I'm able to do more work. So I think that that's the second part to consider, is the task versus the purpose of the job.
- SGSarah Guo
This example really strikes home because my, uh, my sister-in-law, Erin, actually leads, um, nuclear medicine at Stanford, right?
- JHJensen Huang
Mm.
- SGSarah Guo
So she's in radiology, and with all the technology advancements that are coming, these doctors really welcome it, and they are working twenty hours a day trying to do more research and serve more patients.
- JHJensen Huang
Exactly.
- SGSarah Guo
And, and i, 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-
- JHJensen Huang
That's right
- SGSarah Guo
... that we, we need in society-
- JHJensen Huang
That's right
- SGSarah Guo
... like better healthcare.
- JHJensen Huang
That's right.
- SGSarah Guo
I don't think anybody feels like, "You know what? We have reached the, the tip-top, uh, mountaintop of, like, what American healthcare or global healthcare could be."
- JHJensen Huang
Exactly, exactly.
- SGSarah Guo
And, um, the more we can make these people productive-
- JHJensen Huang
Exactly
- SGSarah Guo
... the more demand there will be.
- 12:31 – 15:14
Solving Labor Shortages with Robotics
- JHJensen Huang
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.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
This is, this is not only United States, all over the world, as you guys know.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
And so we're gonna cover the labor shortage. But the second part that people forget, and, and as a result, will-
- EGElad Gil
And there are, there are shortages as well in other places that people talk about AI being relevant. Accounting would be an example where there's shortages there. Nursing is another example. So-
- JHJensen Huang
Severe shortage.
- EGElad Gil
You know, you can, you can go through multiple other industries and say, "Okay, there's gaps."
- JHJensen Huang
That's right.
- EGElad Gil
And AI is trying to help fill those gaps.
- JHJensen Huang
That's exactly right. And so, so, um, automation's gonna help us increase and solve 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.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
And if you look at the robotaxis that are, that are even on the streets today, it's taken ten years for that to happen. Look at all the maintenance crews and all of the, the, the various, you know, uh, hubs that they're in, where you have to take care of these robotaxis, and just imagine, we have a billion robots.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
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.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
And this is the part where you said, um, "When we create this type of automation, we create this other job."
- EGElad Gil
Mm-hmm.
- JHJensen Huang
Right now, look at AI is creating so many jobs.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
The AI industry is creating a boom of jobs.
- SGSarah Guo
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 gonna 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 sucking sound toward all of this demand that is latent in the places where we have gaps-
- JHJensen Huang
Mm-hmm
- SGSarah Guo
... where, um, I, I think a lot of policymakers 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 fulfilling.
- JHJensen Huang
No question.
- SGSarah Guo
Yeah.
- JHJensen Huang
And in the case of lawyer, what's the, what's the purpose of the lawyer versus the task of the lawyer?
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
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-
- SGSarah Guo
Mm-hmm
- 15:14 – 18:39
The Layer Cake of AI Technology
- EGElad Gil
Yeah, the other big theme of the year that you mentioned that I think is really important to touch upon is both, uh, China as sort of a, and 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 closed-source side, it's still a lot of the US models, but things like Qwen, DeepSeek, et cetera-
- JHJensen Huang
Mm-hmm
- EGElad Gil
... are doing very well. You've long been a proponent for open source in general. Could you, could you share your 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?
- JHJensen Huang
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 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 with mechatronics technology to embody that mechatronics and, and make it perform tasks.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
So that's what's AI: automation. But what, what is the stack that makes AI possible? What's the technology stack, the functional stack? And of course, the e-- the, the easiest way to think about that is, is kind of like a five-year, five-year, five-layer cake, which is, at the lowest level, is energy.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
Um, it transforms energy to the output that I just described. The next layer is chips. The next layer is infrastructure, and that infrastructure is both hardware, software, right?
- EGElad Gil
Mm-hmm.
- JHJensen Huang
This is where land, power, and shell, this is where construction is, data centers are. The software stack-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... 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.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
We know this, but it's really helpful to understand that AI is a system of models-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... and AI is a, um, a technology that understands information. And there's human information, and so we oftentimes think about AI as a chatbot.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
But remember, there's biological information, there's chemical information, there's physical information, information of all kinds. There's financial information, there's healthcare information, there's fi-- 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.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
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.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
And, uh, 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 stack-... is one way of thinking about it, and then the next way of thinking about it, I just mentioned, is AI is really diverse.
- 18:39 – 21:52
The Importance of Open Source
- JHJensen Huang
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, 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 to do with their business models is, is really, in the final analysis, 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 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, uh, manufacturing or transportation or, um, it could be in healthcare, without open source today, all of that AI work would be suffocated.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
And so they just need to have something that's pre-trained. They need to have some fundamental technology about 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 f- really miss is the just, the incredible pervasiveness and the importance of open source to all of these industries. Large companies-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... uh, without, without open source, some of, some of hundred-year-old companies that I work with-
- EGElad Gil
Mm-hmm
- JHJensen Huang
-in, in industrial spaces and healthcare spaces, they would be suffocated. They wouldn't be able to do their work.
- EGElad Gil
Yeah, open source at this point is driving all of our data centers. It's driving a big chunk of telephony in the world in terms of Android or other devices. It's driving-
- JHJensen Huang
Exactly
- EGElad Gil
... no different than a lot of the industrial applications. So it, it's already pervasive, and then I think the big question is-
- JHJensen Huang
Open source, without open source, higher ed.
- EGElad Gil
Higher ed wouldn't happen, and so-
- JHJensen Huang
Education research.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
Startups, I mean, the list goes on.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
You know? And so, so we talk, we talk all day long about the tip, 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 spend a lot of time, uh, educating, e- educating, uh, uh, policymakers to help them understand, whatever you decide, whatever you do, don't forget open source. Whatever you decide, whatever you do, don't forget biology.
- EGElad Gil
Mm-hmm.
- SGSarah Guo
I think the counter-narrative here that is worth addressing is that essentially, like, you know, that there should be a monolithic vertical player and monolithic asset in the, like, one model that does it all-
- JHJensen Huang
Mm-hmm
- SGSarah Guo
... and that we can't give away that crown jewel to other countries or non-American companies.
- JHJensen Huang
Mm-hmm.
- SGSarah Guo
A- 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?
- JHJensen Huang
Mm-hmm.
- SGSarah Guo
The capability to deliver any piece of it.
- 21:52 – 23:54
The Myth of “God AI” and Monolithic Models
- JHJensen Huang
I guess someday we will have God AI.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
But that's-
- SGSarah Guo
When is that day?
- JHJensen Huang
But, but that someday, that someday is probably on B- 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.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
And, um, uh, I don't think any company practically believes they're anywhere near God AI, and, uh, nor, nor do I, do I see any researchers having any reasonable ability to create God AI. The ability to under- understand human language and genome language and molecular language and protein language and amino acid language and physics language all supremely well.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
That God AI just doesn't exist, and, and yet we have a lot of industries that need AI.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
AI is, if, if you will, at the simplistic level, it's just the next computer industry.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
And give me an example of a company, an industry, a nation who doesn't need computers.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
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.
- SGSarah Guo
Okay, that's a great-
- JHJensen Huang
And God AI, God AI is not, not gonna 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-
- SGSarah Guo
Mm-hmm
- JHJensen Huang
... country, nation-state that has God AI is just-
- SGSarah Guo
It's unhelpful
- JHJensen Huang
... it's unhelpful. It's too extreme.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
Then, in fact, i- 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 they doing policies? God AI is gonna 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 ought to bring things back to the ground, ground level, and start thinking about things practically and, and use common sense, and
- 23:54 – 29:25
Addressing the “Doomer” Narrative and Regulation
- JHJensen Huang
realize-
- EGElad Gil
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 gonna be able to work again. It's God AI is gonna solve every problem.
- JHJensen Huang
Yeah.
- EGElad Gil
It's we shouldn't have open source for X, Y, Z reason, despite open source powering much of our industries already.
- JHJensen Huang
That's right.
- EGElad Gil
And so it seems like in general, maybe one of the themes of twenty twenty-five was there's a lot of extremes [chuckles] that were sort of painted in the public with AI, that, uh, if you look at them very closely, don't really follow a logical chain in terms of happening anytime soon. Yeah, and so it's, it's, it sounds like it's really important to have this conversation, uh, you know-
- JHJensen Huang
Ex- extremely hurtful, frankly, and I, I think we've done a lot of damage, uh, with very well-respected people, um, who have, who have painted a doom, doomer-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... uh, narrative, um, end-of-the-world narrative-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... science fiction narrative.... and, um, you know, and, and, and I appreciate that, that many of us grew up and, and enjoyed science fiction.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
Um, but, but it's not helpful. It's not helpful to people, it's not helpful to the industry, it's not helpful to society, it's not helpful to the governments.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
There are a lot of, um, many people in the government who obviously aren't as familiar with-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... as, as comfortable with the technology.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
And when PhDs of this, and CEOs of that-
- EGElad Gil
Mm-hmm
- JHJensen Huang
- goes to governments and explain and describe these end-of-the-world scenarios, and extremely-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... extremely dystopian future, the future, um, uh, you have to ask yourself, you know, what is the purpose of that narrative?
- EGElad Gil
Mm-hmm.
- JHJensen Huang
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?
- EGElad Gil
Mm-hmm.
- JHJensen Huang
For what reason would they be doing that? You know, and so, of course-
- EGElad Gil
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?
- JHJensen Huang
You know, I c- I can't, I can't, um, uh, guess what they, what they have in mind. I know that the concern is regulatory capture.
- EGElad Gil
Mm-hmm.
- 29:25 – 35:09
The Plummeting Cost of Compute and Tokenomics
- JHJensen Huang
And so-
- EGElad Gil
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.
- JHJensen Huang
Mm-hmm.
- EGElad Gil
And so I, I think, um, in twenty twenty-four, the, the cost of GPT-４ equivalent models, uh, if you look at a million tokens, it came down over one hundred X. Um, you know, somebody on 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 the NVIDIA level, but also just across the stack, people have been getting big efficiency gains.
- JHJensen Huang
Yeah.
- EGElad Gil
Um, at the same time, model companies are talking about how the costs are rising, how there's enormous sort of capital moats 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?
- JHJensen Huang
I forget the statistic that... But, but you know, Andre, Andrej Karpathy, um, estimated the cost of building the first ChatGPT, I think-
- EGElad Gil
Yeah, yeah
- JHJensen Huang
... versus now. I, I, I think you could do that on a PC now.
- EGElad Gil
Yeah, yeah.
- JHJensen Huang
Okay, so-
- EGElad Gil
It's probably tens of thousands of dollars at this point-
- JHJensen Huang
Yeah
- EGElad Gil
... or maybe even less.
- JHJensen Huang
Right.
- EGElad Gil
Yeah.
- JHJensen Huang
And so it costs nothing.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
And, and, uh-
- EGElad Gil
He has an open source project that you can do in a weekend.
- JHJensen Huang
Oh, is that right?
- EGElad Gil
Yeah.
- JHJensen Huang
Okay.
- EGElad Gil
Yeah.
- JHJensen Huang
That's incredible, right?
- EGElad Gil
Yeah.
- JHJensen Huang
We're talking about three years.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
Mm-hmm. What people, people said cost billions of dollars, um, supercomputers built, raising billions of dollars in order to do all that, now-
- EGElad Gil
Mm-hmm
- 35:09 – 37:49
The Return to Research
- SGSarah Guo
And I think one thing that felt very different to me about twenty twenty-five is, um, Ilya, uh, said recently that, uh, you know, we're in the age of research again-
- JHJensen Huang
Yeah
- SGSarah Guo
-versus an age of scaling. I think both things are happening, by the way.
- JHJensen Huang
Yeah.
- SGSarah Guo
Everybody is also trying to scale on multiple dimensions.
- JHJensen Huang
Yeah, exactly.
- SGSarah Guo
But-
- JHJensen Huang
Both are happening
- SGSarah Guo
... you know, being six months behind or being at a hundred versus a two hundred K cluster-
- JHJensen Huang
Mm-hmm
- SGSarah Guo
-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-
- JHJensen Huang
That's right
- SGSarah Guo
-approach from here or who are working on diversity of problems.
- JHJensen Huang
That's right.
- SGSarah Guo
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 LLMs.
- JHJensen Huang
Yeah, and several, several other dynamics. Um, as the market grows, each one of these models could choose to have verticals-
- SGSarah Guo
Mm-hmm
- JHJensen Huang
-or segments where they want to differentiate.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
Somebody could decide to be a better coder. Somebody could decide to be just better at being easier to be accessible so that it could be a greater consumer product.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
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.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
It's no longer necessary to boil the entire ocean. The fir-- Two years ago, because it was called pre-training, pre, uh, you know, people, people said: "Well, you know, pre-training is over." Uh, 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, I think it's just training, but pre-training is tr- pre-training-
- SGSarah Guo
Yeah.
- JHJensen Huang
-and, and therefore it's training. Training, as you, as, as we all know, is, is where, uh, compute scaling directly translates to intelligence. You've, you've, you've largely-- now, now the s- the, the data, the, the data necessary to train the model is actually pretty small. Maybe it's just the 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.
- SGSarah Guo
Mm.
- JHJensen Huang
And we become superhuman or incredibly good at something that other people are not. And so I think AI labs are gonna start doing the same. They're gonna start bifurcating into various segments, and over time, you're gonna... And startups will do the same.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
They'll find a micro niche, and they'll take something open and then be incredibly
- 37:49 – 43:20
Future of Coding and Software Engineering
- JHJensen Huang
good at it.
- SGSarah Guo
Well, I think one of the most optimistic views here is, uh, actually, that these micro niches are quite valuable, right? I was talking to Andre, um, because we've been talking to a lot of people about their predictions for next year. We'll ask you yours of-
- JHJensen Huang
Mm.
- SGSarah Guo
-as well, of course. Um, uh, but he asked, you know, "What is a, what's an example of a prediction that would have been prescient last year?"
- JHJensen Huang
Mm-hmm.
- SGSarah Guo
Uh, and my answer, everything's easy in retrospect, is that coding would be the first application-level business-
- JHJensen Huang
Mm.
- SGSarah Guo
-that gets to a billion of ARR-
- JHJensen Huang
Mm-hmm
- SGSarah Guo
... as an AI native app.
- JHJensen Huang
Mm-hmm. Mm-hmm.
- SGSarah Guo
Right? And I, I think if you'd 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.
- JHJensen Huang
Mm-hmm.
- SGSarah Guo
And two is that developer tools never get very big.
- JHJensen Huang
Uh-huh.
- SGSarah Guo
Right?
- JHJensen Huang
Mm-hmm.
- SGSarah Guo
Well, it kind of depends on how valuable the developer tool is. Now, I think many more people understand software engineering isn't a niche, and there's more demand than ever for it.
- JHJensen Huang
Mm-hmm. Mm-hmm.
- SGSarah Guo
But I think we'll see more like that next year.
- JHJensen Huang
And also interesting, uh, we are using, we, we use Cursor here, and we use Cursor pervasively here. Every engineer uses it.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
And a number of engineers, you, you just mentioned it, the number of people we're hiring today is just incredible.
- SGSarah Guo
Yeah.
- JHJensen Huang
Right? Monday is come to work at NVIDIA day.
- SGSarah Guo
[chuckles]
- JHJensen Huang
And, and, um, uh, why is that? Uh, this is now the purpose and the task.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
The purpose of a software engineer is to solve known problems and to find new problems to solve. Coding is one of the tasks.
- SGSarah Guo
Mm-hmm.
- 43:20 – 46:00
The Industries Due For Their “ChatGPT” Moments
- JHJensen Huang
tokens on.
- EGElad Gil
But as, as costs drop, usually you open up new applications or new verticals that become more and more accessible.
- JHJensen Huang
Mm-hmm.
- EGElad Gil
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-
- JHJensen Huang
Because of three things, I- because of, uh, uh, because of a, a couple, two, three things, I, I think, I think several industries are gonna g- are gonna experience their ChatGPT moment. Um, I believe that multi-modality and, um, very long context is going to enable, of course, really, really cool chatbots. Um, but the basic architecture, that in combination with breakthroughs in synthetic data generation, is going to help create the ChatGPT moment for digital biology.
- EGElad Gil
Mm-hmm. Mm-hmm.
- JHJensen Huang
That moment is coming.
- EGElad Gil
And by digital biology, do you specifically mean other aspects of, like, protein folding and protein binding, or do you mean synthesis?
- JHJensen Huang
Protein synthesis.
- EGElad Gil
I see, protein synthesis.
- JHJensen Huang
I think we're good at protein understanding.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
Now, multi-protein understanding is coming online, and we recently created a model called LaProteina. It's open. Um, it's for multi-protein-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... 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 to advance very quickly. ChatGPT moment of proteins.
- SGSarah Guo
Yeah, there are a lot of interesting companies working on molecule design and end-to-end way, like Chai.
- JHJensen Huang
That's right. Exactly.
- SGSarah Guo
Yeah.
- JHJensen Huang
And then, and then, of course, chemical understanding and chemical generation.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
And then protein chemical-
- SGSarah Guo
Yeah
- JHJensen Huang
... confirmation, understanding, and generation.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
Is that right? And so that combination, the ChatGPT moment, the generative AI moment, all of that stuff is coming together for, for, um, digital biology.
- SGSarah Guo
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.
- JHJensen Huang
Mm-hmm.
- SGSarah Guo
All of these things around biology and chemistry and material science, they require real-world data generation and experimentation-
- JHJensen Huang
Right, and-
- SGSarah Guo
... and that's new infrastructure, too
- 46:00 – 54:06
The Evolution of Self-Driving Cars and Robotics
- JHJensen Huang
The, the, the, 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-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... and planning cars, they're going to be reasoning cars. So these cars are going to be thinking all the time, and when they come up, they come up to a, a circumstance they, they've never en- encountered before, they can break it down into circumstances they have encountered before-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... 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-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... 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're 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 humanoid robots or multi-embodiment robots. You know, it does-
- EGElad Gil
What do you think is the-
- JHJensen Huang
... it doesn't have to be human.
- EGElad Gil
What do you think is the timeframe for that? Because if you look at the self-driving analog-
- JHJensen Huang
Mm-hmm
- EGElad Gil
... and obviously, self-driving technologies were based on very different types of neural networks than what we're using today-
- JHJensen Huang
Yeah
- EGElad Gil
... in terms of, you know, there's been a big swap over the last two, three years-
- JHJensen Huang
Yeah
- EGElad Gil
... in terms of how we do a lot there, um-
- JHJensen Huang
We started too soon.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
Self-driving cars really have four eras. The first era was smart sensors-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... connected into a car.
- EGElad Gil
Mm-hmm.
- SGSarah Guo
The Mobileye era.
- JHJensen Huang
The Mobileye era.
- SGSarah Guo
Yeah.
- JHJensen Huang
And, and even, even the very earliest days-
- SGSarah Guo
Mm-hmm
- JHJensen Huang
... of, of, uh, Waymo.
- SGSarah Guo
ADAS, yeah.
- JHJensen Huang
Yeah, even the earliest days of Waymo. The, the, um, you're talk- you're using smart sensors, um, a lot of human-engineered algorithms-
- 54:06 – 58:49
Energy Demand and Growth for AI
- EGElad Gil
So I guess, yeah, one of the other narratives from... We're looking at narratives that are true versus not true, you know, for twenty five. 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 you, how do you think about that?
- JHJensen Huang
On the first week of President Trump's administration, he said, "Drill, baby, drill," and he took so much flak for that.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
If not for this entire change in, in sentiment about energy growth in our country-
- EGElad Gil
Mm-hmm.
- SPSpeaker
Mm-hmm
- JHJensen Huang
... we can all concede now we would have handed this industrial revolution to somebody else.
- EGElad Gil
Mm-hmm.
- SPSpeaker
Mm. And we're still power constrained.
- JHJensen Huang
We're still power constrained.
- SPSpeaker
Yeah.
- JHJensen Huang
Without energy, there can be no new industry.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
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.... Mm-hmm. Without energy, you can't have industrial growth. Without industrial growth, the c- the nation can't be more prosperous. Without being more prosperous, we can't take care of domestic issues, we can't take care of social issues-
- EGElad Gil
Mm-hmm.
- JHJensen Huang
-you know, on and on and 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, we need to be-- we, of course, we need more energy on the grid. We need more energy behind the meter. Uh, we're gonna need nuclear. Uh, wind is not gonna be enough; solar is not gonna be enough. Let's just all ack- acknowledge that we'll take it, we'll take everything we can, um, but the fact of the matter is, I think for the, for the next decade-
- EGElad Gil
Mm-hmm.
- JHJensen Huang
-natural gas, you know, is probably the, the only way to go forward.
- SGSarah Guo
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 'twenty-seven and 'twenty-eight, 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-
- JHJensen Huang
Mm-hmm.
- SGSarah Guo
Right?
- JHJensen Huang
Mm-hmm.
- SGSarah Guo
Because people look at the demand-
- JHJensen Huang
Finally, that's right, demand signal.
- SGSarah Guo
They look at the demand-
- JHJensen Huang
That's right.
- SGSarah Guo
-and the demand is driving people to create massive new battery companies-
- JHJensen Huang
That's right. Exactly.
- SGSarah Guo
-solar concentrators. It's putting new energy behi-- new energy, like, you know, willpower-
- JHJensen Huang
It's so interesting-
- 58:49 – 1:04:43
2026 Outlook: US-China Relations and Geopolitics
- JHJensen Huang
ideas.
- EGElad Gil
How do you... So I guess we've talked a lot about 'twenty-five-
- JHJensen Huang
Mm-hmm
- EGElad Gil
... and the narrative is 'twenty-five. How do you think about 'twenty-six? What are you excited about? What do you see coming? What do you think are big changes that we should be aware of?
- JHJensen Huang
I am optimistic that, that, um, our relationship with China will improve.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
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. Uh, that, that they're an adversary-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... 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, i- is 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.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
Um, both countries ought to, ought to invest in their own independence. Um, uh, you know, when you depend too much on someone, the relationship becomes too emotional-
- SGSarah Guo
[chuckles]
- JHJensen Huang
-uh, as you know. [chuckles] And so it's good to have some independence and, or as much independence as either, either would like, um, but to recognize that there's a lot of coupling, a lot of de- 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 answer, and, 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 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, i- 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 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, 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, uh, requires a nuanced strategy.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
And, and, um, uh, 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-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... and to, to, uh, support American technology leadership on the one hand, um, to win globally.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
And, and then, and then China, of course, is sorting itself out. You know, uh, I mean, not, not sorting, but they're sorting out the attitude about how to think about American technology. And there-
- EGElad Gil
Because the historical argument there has been that if, if you look, for example, at the internet-
- JHJensen Huang
Yeah
- EGElad Gil
... um, there was what was known as a great firewall, right? China basically-
- JHJensen Huang
Yeah
- EGElad Gil
... prevented US competition into China, while the opposite wasn't as true.
- JHJensen Huang
Yeah.
- EGElad Gil
Um, there's been mass, uh, 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 been perceived to exist between the two countries.
- JHJensen Huang
The way that I would think through that is go back to the first principles of technologies again.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
And let's say the internet, you have the chip industry, you have the systems in- 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-
- EGElad Gil
Mm-hmm
- 1:04:43 – 1:16:20
Is There An AI Bubble?
- SGSarah Guo
Mm-hmm. Jensen, my other investor friends will not forgive me if I don't ask you-
- JHJensen Huang
That's okay
- SGSarah Guo
... about twenty twenty-six. Um, uh, on the business side, uh, are we in an AI bubble?
- JHJensen Huang
AI bubble?
- SGSarah Guo
Yeah.
- JHJensen Huang
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 ex- invented accelerated computing. Accelerated computing does computer graphics and rendering. AI doesn't. Um, accelerated computing does data processing, SQL data processing. AI doesn't.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
Um, uh, accelerated computing does molecular dynamics and quantum chemistry. AI doesn't. Uh, it, you know, all- these are all things that people could say, "Someday, AI will," but it doesn't today. Uh, accelerated computing is really essential for, uh, classical machine learning, XGBoost, recommender systems, the whole process of, uh, feature engineering, extract, load, and transform.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
That entire data science, machine learning lifecycle, 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 a shift from general-purpose computing-
- EGElad Gil
Mm-hmm
- JHJensen Huang
... to accelerated computing because Moore's Law is largely ended. You can't use CPUs for everything anymore, like you used to, and so it's just n- no longer productive enough. It's not deflationary enough.
- SGSarah Guo
Mm-hmm.
- JHJensen Huang
And so, so we have to move towards a new computing model, and that's where accelerated comes in. If you, if generative AI... Well, excuse me, if chatbots, let's just go, you know, OpenAI and Anthropic and Gemini, if none of that existed today, NVIDIA would be a multi-hundred billion dollar company, and the reason for that is because, as you know, the foundation of computing is shifting-
- SGSarah Guo
Mm-hmm
- JHJensen Huang
... 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 A- AI bubble question, and we always go back to OpenAI's revenues, a hundred percent, don't we?
- EGElad Gil
Mm-hmm.
- JHJensen Huang
You ask somebody, "Hey, is there an AI bubble?" Everybody just, zoop, goes directly to OpenAI's revenues. First of all, if OpenAI currently has twice the capacity, their revenues would double. You guys know that.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
If they have ten times the capacity, their, I really believe their revenues were ten times. And so they 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 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.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
And if they don't have factory capacity, how do 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 ten billion dollars. Nobody ever talks about that.
- SGSarah Guo
[chuckles]
- JHJensen Huang
And bec- you have to train world models, you have to train these AI- AVs, and it's happening, robotaxis happening all over the world. Our AI work with, uh, digital biology, our AI work in financial services, the whole industry of quants, quantitative trading, is moving towards-
- SGSarah Guo
A huge shift.
- JHJensen Huang
Yeah! Exactly. There used to be classical machine learning, a whole bunch of human-featured-
- SGSarah Guo
Mm-hmm
- JHJensen Huang
... they call 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 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 one hundred trillion dollars in GDP. Out of that, let's just say two percent. Two percent 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.
- EGElad Gil
Mm-hmm.
- JHJensen Huang
Today, what are they do- doing? Building supercomputers.
Episode duration: 1:16:20
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