No Priors: Artificial Intelligence | Technology | Startups - NVIDIA's Jensen Huang on AI Chip Design, Scaling Data Centers, and his 10-Year Bets

Episode Date: November 7, 2024

In this week’s episode of No Priors, Sarah and Elad sit down with Jensen Huang, CEO of NVIDIA, for the second time to reflect on the company’s extraordinary growth over the past year. Jensen discusses AI’s takeover of datacenters and NVIDIA’s rapid development of x.AI’s supercluster. The conversation also covers Nvidia’s decade-long infrastructure bets, software longevity, and innovations like NVLink. Jensen shares his views on the future of embodied AI, digital employees, and how AI is transforming scientific discovery. Sign up for new podcasts every week. Email feedback to [e-mail] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Nvidia Show Notes:  00:00 Introduction 1:22 NVIDIA's 10-year bets 2:28 Outpacing Moore’s Law 3:42 Data centers and NVLink 7:16 Infrastructure flexibility for large-scale training and inference  10:40 Building and optimizing data centers  13:30 Maintaining software and architecture compatibility  15:00 X.AI’s supercluster  18:55 Challenges of super scaling data centers 20:39 AI’s role in chip design  22:23 NVIDIA's market cap surge and company evolution  27:03 Embodied AI 28:33 AI employees 31:25 Impact of AI on science and engineering  35:40 Jensen’s personal use of AI tools

Hi listeners and welcome to No Priors. Today we're here again, one year since our last discussion with the one and only Jensen Huang, founder and CEO of NVIDIA. Today, NVIDIA's market cap is over $3 trillion, and it's the one literally holding all the chips in the AI revolution. We're excited to hang out in NVIDIA's headquarters and talk all things frontier models and data center scale computing, and the bets NVIDIA is taking on a 10-year basis. Welcome back, Jensen. 30 years in to Envidia and looking 10 years at, what are

the big bets you think are still to make? Is it all about scale up from here? Are we running into limitations in terms of how we can squeeze more compute memory out of the architectures we have? What are you focused on? Well, if we take a step back and think about what we've done, we went from coding to machine learning, from writing software tools, to creating AI. and all of that running on CPUs that was designed for human coding to now running on GPUs designed for AI coding, basically, machine learning. And so the world has changed. The way we do computing, the whole stack has changed.

And as a result, the scale of the problems we could address has changed a lot. Because if you could paralyze your software on one GPU, you've set the foundations to paralyze across a whole cluster. or maybe across multiple clusters or multiple data centers. And so I think we've set ourselves up to be able to scale computing at a level and develop software at a level that nobody's ever imagined before. And so we're at the beginning of that.

Over the next 10 years, our hope is that we could double or triple performance every year at scale, not at chip, at scale. and to be able to, therefore, drive the cost down by a factor of two or three, drive the energy down by a factor of two, three, every single year. When you do that every single year, when you double or triple every year, in just a few years it adds up. And so it compounds really, really aggressively. And so I wouldn't be surprised if, you know, the way people think about Moore's Law,

which is 2X every couple of years, you know, we're going to be on some kind of a hyper Moore's Law curve. and I fully hope that we continue to do that. What do you think is the driver of making that happen even faster than Morsal? Because then Morsal was sort of self-reflexive, right? It was something that he said, and then people kind of implemented it to make it happen. Yeah. The two fundamental technical pillars, one of them was Dinar scaling,

and the other one was Carver Mead's VLSI scaling. And both of those techniques were rigorous techniques, but those techniques have really run out of sense. steam. And so now we need a new way of doing scaling. You know, obviously the new way of doing scaling are all kinds of things associated with code design. Unless you can modify or change the algorithm to reflect the architecture of the system and then change the system to reflect the architecture of the new software and go back and forth. Unless you can control both sides of it, you have no hope. But if you can control both sides of it, you can do things like move from

FP-64 to FP-32 to BF-16 to FP8 to, you know, FP4 to who knows what, right? And so I think that code design is a very big part of that. The second part of it, we call it full stack. The second part of it is data center scale, you know, unless you could treat the network as a compute fabric and push a lot of the work into the network, push a lot of the work into the fabric and as a result you you're compressing you know doing compressing at very large scales and so that that that's the reason why we bought melanox and started fusing infiniban and mv link um in such an aggressive way and now look where md link is going to go you know the compute fabric is going to going to scale out what appears to be one incredible processor called a GP

Now we've got hundreds of GPUs that are going to be working together. You know, most of these computing challenges that we're dealing with now, one of the most exciting ones, of course, is inference time scaling. It has to do with essentially generating tokens at incredibly low latency. Because you're self-reflecting, as you just mentioned. I mean, you're going to be doing tree surge. You're going to be doing chain of thought. you're going to be doing probably some amount of simulation in your head.

You're going to be reflecting on your own answers. Well, you're going to be prompting yourself and generating text to your, you know, silently and still respond, hopefully, in a second. Well, the only way to do that is if your latency, your latency is extremely low. Meanwhile, the data center is still about producing high throughput tokens because, you know, you still want to keep cost down. You want to keep the throughput high. You want to generate a return. And so these two fundamental things about a factory, low latency and high throughput, they're at odds with each other. And so in order for us to create something that is really great in both, we have to go invent something new.

And NVLink is really our way of doing that. Now you have a virtual GPU that has incredible amount of flops because you need it for context. You need a huge amount of memory, working memory, and stuff. still have incredible bandwidth for token generation, all of the same time. I guess in parallel, you also have all the people building the models, actually also optimizing things pretty dramatically. Like, David on my team pulled data over the last 18 months or so. The cost of a million tokens going into a GPT4 equivalent model is basically dropped 240X.

And so there's just massive optimization and compression happening on that side as well. Just in our layer, just on the layer that we work on. You know, one of the things that we care a lot about, of course, is the ecosystem of our stack and the productivity of our software. You know, people forget that because you have Kuda Foundation, and that's a solid foundation, everything above it can change. If everything, if the foundation's changing underneath you, it's hard to build a building on top. It's hard to create anything interesting on top. And so could have made it possible for us to iterate so quickly just in the last year, I think we just went back and benchmarked when Lama first came. out, we've improved the performance of Hopper by a factor of five without the layer on top

ever-changing. Now, well, a factor of five in one year is impossible using traditional computing approaches, but accelerated computing and using this way of code design, we're able to invent all kinds of new things, yeah. How much are your biggest customers thinking about the interchangeability of their infrastructure between large-scale training and inference? Well, you know, infrastructure is disaggregated these days. Sam was just telling me that he had decommissioned Volta just recently.

They have Pascal's, they have Ampiers, all different configurations of blackwall coming. Some of it is optimized for air cool. Some of it's optimized a liquid cool. Your services are going to have to take advantage of all of this. The advantage that Nvidia has, of course, is that the, you know, the, you know, you know, infrastructure that you built today for training will just be wonderful for inference tomorrow. And most of ChatsupT, I believe, are inferenced on the same type of systems that were trained on just recently. And so if you can train on it, you can inference on it. And so you're leaving,

you're leaving a trail of infrastructure that you know is going to be incredibly good of inference. And you have complete confidence that you can then take that return on the investment that you've had and put it into a new infrastructure to go scale with. You know you're going to leave behind something of use. And you know that NVIDIA and the rest of the ecosystem are going to be working on improving the algorithm so that the rest of your infrastructure improves by a factor of five, you know, in just a year. And so that motion will never change.

And so the way that people will think about the infrastructures, yeah, even though I built it for training today, it's got to be great for training, we know it's going to be great for inference. So, inference is going to be multi-scale. I mean, you're going to take, first of all, in order to distill smaller models, good to have a larger model that's still from. And so you're still going to create these incredible frontier models. They're going to be used for, of course, the groundbreaking work.

You're going to use it for synthetic data generation. You're going to use the big models to teach smaller models and distill down the smaller models. And so there's a whole bunch of different things you can do. But in the end, you're going to have giant models all the way down. little tiny models. The little tiny models are going to be quite effective, you know, not as generalizable, but quite effective. And so, you know, they're going to perform very specific stunts incredibly well, that one task. And we're going to see superhuman task in one little tiny domain from a little tiny, tiny model. Maybe, you know, it's not a small language model, but, you know,

tiny language model, TLMs or, you know, whatever. Yeah. So I think we're going to see all kinds of sizes and we hope. Is that right? just kind of like softwares today. I think in a lot of ways, artificial intelligence allows us to break new ground in how easy it is to create new applications. But everything about computing has largely remained the same. For example, the cost of maintaining software

is extremely expensive. And once you build it, you would like it to run on a large of an installed base as possible. You would like not to write the same software twice. I mean, you know, a lot of people still feel the same way. You like to take your engineering and move them forward. And so to the extent that the architecture allows you to on one hand, create software today that runs even better tomorrow with new hardware, that's great.

Or software that you create tomorrow, AI that you create tomorrow, it runs on a large installed base. You think that that's great. That way of thinking about software is not going to change. NVIDIA has moved into larger and larger, let's say, like a unit of support for customers, I think about it going from single chip to, you know, server to rack and VL 72. How do you think about that progression? Like what's next?

Like should In video do full data center? In fact, we built full data centers. The way that we build everything, unless you're building, if you're developing software, you need the computer in its full manifestation. We don't build PowerPoint slides and ship the chips. And we build a whole data center. And until we get the whole data center built up, how do you know the software works?

Until you get the whole data center built up, how do you know your, you know, your fabric works and all the things that you expect it, the efficiencies to be, how do you know it's going to really work at the scale? And that's the reason why it's not unusual to see somebody's actual performance be dramatically lower than their peak performance

as shown in PowerPoint slides. And computing is just not used to, it's not what it used to be. You know, I say that the new unit of computing is the data center. That's to us. So that's what you have to deliver. That's what we built. Now, we build a whole thing like that. And then we, for every single thing, every combination, air cooled, x86, liquid cooled, grace, Ethernet, Infineband, MVLink, no MV link.

You know what I'm saying? We build every single configuration. We have five supercomputers in our company today. next year we're going to build easily five more. So if you're serious about software you build your own computers. If you're serious about software, then you're going to build your whole computer and we build it all at scale. This is the part

that is really interesting. We build it at scale and we build it vertically integrate. We optimize it full stack and then and then we disaggregate everything and we sell it in parts. That's the part that is completely utterly remarkable about

what we do. The complexity of that is just insane. And the reason for that is we want to be able to graft our infrastructure into GCP, AWS, Azure, OCI. All of their control planes, security planes are all different. And all of the way they think about their cluster sizing, all different. And, but yet we make it possible for them to all accommodate NVIDIA's architecture so that Kuda could be everywhere. That's really, really in the end, the singular thought, you know, that we would like to have a computing platform that developers could use. that's largely consistent, modular 10% here and there

because people's infrastructure are slightly optimized differently. And modular 10% here and there, but everything they build will run everywhere. This is kind of one of the principles of software that it should never be given up. And we protect it quite dearly. It makes it possible for our software engineers to build once, run everywhere.

And that's because we recognize that the investment of software is the most expensive investment, and it's easy to test. Look at the size of the whole hardware industry, and then look at the size of the world's industries. It's $100 trillion on top of this $1 trillion industry, and that tells you something. The software that you build, you have to, you know, you basically maintain for as long as you shall live. We've never given up on a piece of software. The reason why Kudas used is because, you know, I told everybody, we will maintain this for as long as we shall live, and we're serious. And we still maintain, I just saw a review the other day,

Nvidia Shield, our Android TV. It's the best Android TV in the world. We shipped it seven years ago. It is still the number one Android TV that people, you know, anybody who enjoys TV. And we just updated the software just this last week. And people wrote a new story about it. G-Force. We have 300 million gamers around the world.

We've never stranded a single one of them. And so the fact that our architecture is compatible across all of these different areas makes it possible for us to do it. Otherwise, we would be, we would have, you know, we would have software teams that are 100 times the size of our company as today, if not for this architectural compatibility. So we're very serious about that. And it translates to benefits to customers. One impressive substantiation of that recently was how quickly brought up a cluster for X.a.i. Yeah. And if you want to talk about that, because that was striking in terms of both the scale and the speed with which you did that.

You know, a lot of that credit you got to give to Elon. I think the, first of all, to decide to do something, select the site, bring cooling to it, power, and then decide to build this 100,000 GPU supercluster, which is, you know, the largest of its kind in one unit. And then working backwards, you know, we started planning together. The date that he was going to stand everything up, and the date that he was going to stand everything up was determined, you know, quite a few months ago. And so all of the components, all the OEMs, all the systems,

all the software integration we did with their team, all the network simulation. We simulate all the network configurations. I mean, it's like we pre-staged everything as a digital twin. We pre-staged all of his supply chain. chain. We pre-staged all of the wiring of the networking. We even, we even set up a small version of it, kind of a, you know, just a first instance of it, you know, ground truth, if you were reference zero, you know, system zero, before everything else showed up. So by the time

that everything showed up, everything was staged, all the practicing was done, all the simulations were done, and then, you know, the massive integration. Even then, the massive of integration was a monument of, you know, gargantuan teams of humanity crawling over each other, wiring everything up 24-7. And within a few weeks, the clusters were out. I mean, it's really, yeah, it's really a testament to his willpower and how he's able to think

through mechanical things, electrical things, and overcome what is apparently, you know, extraordinary obstacles. I mean, what was done there is the first time that a computer of that large scale has ever been done at that speed. Unless our two teams are working from a networking team to compute team to software team to training team and the infrastructure team, the people that the electrical engineers to the software engineers all working together. Yeah, it's really quite a feat to watch. Was there a challenge that felt most likely to be blocking from an engineering perspective? Just the tonnage of electronics that had to come together.

I mean, it would probably be worth just to measure it. I mean, it's, you know, it tons and tons of equipment. It's just abnormal. You know, usually a supercomputer system like that, you plan it for a couple of years from the moment that the first systems come on, come delivered to the time that you probably submitted everything for some serious work. Don't be surprised if it's a year, you know. I mean, that happens all the time. It's not abnormal. Now, we couldn't afford to do that.

So we created, you know, a few years ago, there was an initiative in our company that's called Data Center as a product. We don't sell it as a product, but we have to treat it like it's a product. Everything about planning for it and then standing it up, optimizing it, tuning it, keep it operational. The goal is that it should be, you know, kind of like opening up your beautiful new iPhone and you open it up and everything just kind of works. Now, of course, it's a miracle of technology making it like that, but we now have the skills to do that.

And so if you're interested in the data center and just have to give me a space and some power, some cooling, you know, and we'll hope you set it up within call it 30 days. I mean, it's pretty extraordinary. That's wild. If you look ahead to 200,000, 500,000, a million in a supercluster or whatever you call it at that point, what do you think is the biggest? blocker. Capital, energy, supply in one area? Everything. Nothing about what you just, the scales that you talked about, nothing is normal. Yeah. But nothing is impossible. Nothing is, yeah, no laws of

physics limits, but everything is going to be hard. And of course, you know, is it worth it? Like, you can't believe, you know, to get to something that we would recognize as, as a computer that so easily and so able to do what we ask it to do, what, you know, otherwise general intelligence of some kind. And even, you know, even if we could argue about, is it really general intelligence, just getting close to it is going to be a miracle. We know that. And so I think there are five or six endeavors to try to get there, right?

I think, of course, Open AI and Anthropic and X and, you know, of course, Google and meta and Microsoft. And, you know, they're, this frontier, the next couple of clicks up that mountain are just so vital. Who doesn't want to be the first on that, on that mountain? I think that the prize for reinventing intelligence altogether is just, it's too consequential not to attempt it. And so I think there are no laws of physics. Everything is going to be hard. A year ago, when we spoke together, you talked about, we asked like what applications you got most excited about that Infidia would serve next in AI and otherwise. And you talked about how you led to your most extreme customers, straight lead you there.

and about some of the scientific applications. I think that's become like much more mainstream of you over the last year. Is it still like science and AI's application of science that most excites you? I love the fact that we have digital, we have AI chip designers. Here at Nvidia. Yeah. I love that we have AI software engineers. How effective are AI chip designers today?

Super good. We can't, we couldn't build, we couldn't build Hopper without it. And the reason for that is because they could explore a much larger, space and we can. And because they have infinite time, they're running on a supercomputer. We have so little time using human engineers that we don't explore as much of the space as we should. And we also can't explore it commentarily. I can't explore my space while including your exploration and your exploration. And so, you know, our chips are so large. It's not like it's designed as one chip. It's designed almost like a thousand ships. And we have to optimize

each one of them, kind of in isolation, you really want to optimize a lot of them together and, you know, cross-module code design and optimize across a much larger space. Obviously, we're going to be able to find local maximums that are hidden behind local minimum somewhere. And so clearly we can find better answers. You can't do that without AI engineers.

Just simply can't do it. We just don't have enough time. One other thing that's changed since we last spoke collectively, and I looked it up. At the time, NVIDIA's market cap was about $500 billion. It's now over $3 trillion. So of the last 18 months, you've added $2.5 trillion plus of market cap, which effectively is $100 billion plus a month, or two and a half snowflakes or, you know, a stripe plus a little bit, or however you want to think about it.

A country or two. A country or two. Obviously, a lot of things have stayed consistent in terms of focus on what you're building and, et cetera. and, you know, walking through here earlier today, I felt the buzz like when I was at Google 15 years ago was kind of you felt the energy of the company and the vibe of excitement. What has changed during that period, if anything?

Or what is different in terms of either how invidia functions or how you think about the world or the size of bets you can take or... Well, our company can't change as fast as a stock price. Let's just be clear about that. So in a lot of ways, we haven't changed that much. I think the thing to do was to take a step back, and ask ourselves, what are we doing?

I think that that's really the big, you know, the big observation, realization, awakening for companies and countries is what's actually happening. I think when we're talking about earlier, from our industry perspective, we reinvented computing. Now, it hasn't been reinvented for 60 years. That's how big of a deal it is. That we've driven down the marginal cost,

of computing, down probably by a million X in the last 10 years, to the point that we just, hey, let's just let the computer go exhaustively write the software. That's the big realization. And that in a lot of ways, we were kind of saying the same thing about chip design. We would love for the computer to go discover something about our chips that we otherwise couldn't have done ourselves, explore our chips, and optimize it in a way that we couldn't do ourselves in the way that we would love for digital biology or you know any other any other field of science and so i i think people are starting to realize when we reinvented we reinvented on

computing but what does that mean even and as we all of a sudden we created this thing called intelligence and and what happened to computing well we went from data centers data centers Data centers are multi-tenant, stores our files. These new data centers we're creating are not data centers. They're not multi-tenant. They tend to be single-tenant. They're not storing any of our files. They're just, they're producing something.

They're producing tokens. And these tokens are reconstituted into what appears to be intelligence. Isn't that right? And intelligence of all different kinds. It could be articulation of robotic motion. It could be sequences of amino acids. It could be, you know, chemical chains. be all kinds of interesting thing, right? So what are we really doing? We've created a new instrument,

a new machinery that in a lot of ways is the noun of the adjective generative AI. You know, instead of generative AI, it's an AI factory. It's a factory that generates AI. And we're doing that at extremely large scale. And what people are starting to realize is, you know, maybe this is a new industry. It generates tokens. It generates numbers. But these numbers constitute in a way that is fairly valuable. And what industry wouldn't benefit from it? Then you take a step back and you ask yourself again, you know, what's going on? Invidia, on the one hand, we reinvent a computing as we know it.

And so there's a trillion dollars of the infrastructure that needs to be modernized. That's just one layer of it. The big layer of it is that there's this instrument that we're building is not just for data centers, which we're modernizing, but you're using it for producing some new commodity. And how big can this new commodity industry be? Hard to say, but it's probably worth trillions. And so that I think is kind of the,

if you were to take a step back. You know, we don't build computers anymore. We build factories. And every country is going to need it. Every company is going to need it. You know, give me an example of a company or industry, has this, you know what? We don't need to produce intelligence.

We've got plenty of it. And so that's the big idea, I think, you know, and that's kind of an abstracted industrial view. And, you know, someday, someday people will realize that in a lot of ways the semiconductor industry wasn't about building chips. It was about building the foundational fabric for society. And then all of a sudden, everybody goes, oh, I get it. You know, this is a big deal. It's not just about chips. How do you think about embodiment now?

Well, the thing I'm super excited about is in a lot of ways, we're close to artificial general intelligence, but we're also close to artificial general robotics. Tokens are tokens. I mean, the question is, can you tokenize it? Of course, tokenizing things is not easy, as you guys know. But if you were able to tokenize things, align it with large language models and other modalities, if I can generate a video, that has Jensen reaching out to pick up the coffee cup, why can't I prompt a robot to generate the tokens to pick up the, you know?

And so intuitively you would think that the problem statement is rather similar for a computer. And so I think that we're that close. That's incredibly exciting. Now, the two Brownfield robotic systems, brownfield means that you don't have to change the environment for, is self-driving cars and with digital chauffeurs

and embodied robots between the cars and the human robot we could literally bring robotics to the world without changing the world because we built a world for those two things probably not a coincidence

that Elon's focused in those two forms of robotics because it is likely to have the larger potential scale and so I think that's exciting but the digital version of it is equally exciting. You know, we're talking about

digital or AI employees. There's no question we're going to have AI employees of all kinds and our outlook will be some biologics and some artificial intelligence and we'll prompt them

in the same way. Isn't that right? Mostly I prompt my employees, you know, provide them context, ask them to perform a mission. They go and recruit other team members. They come back and

we're going back and forth. How is that going to be? any different with digital and AI employees of all kinds. So we're going to have AI marketing people, AI should designers, AI supply chain people, AI, you know. And I'm hoping that Nvidia is someday biologically bigger, but also from an artificial intelligence perspective, much, much bigger. That's our future company.

If we came back and talked to you a year from now, what part of the company do you think would be most artificially intelligent? I'm hoping is chip design. Okay. Most important part. That's right. Because I should start, I should start where it moves the needle most. Also, where we can make the biggest impact most.

You know, it's such an insanely hard problem. I work with Sassine at Synopsis and Root at Cadence. I totally imagine them having synopsis chip designers that I can rent. And they know something about a particular module. their tool and they trained an AI to be incredibly good at it and we'll just hire a whole bunch of them whenever we need we're in that phase of that chip design you know i might might rent a million synopsis engineers to come and help me out and then go rent a million cadence engineers to help me out and that what an exciting future for them that they have all these agents that that sit on top

of their tools platform that use the tools platform and other and collaborate with with other platforms And you'll do that for, you know, Christian will do that at SAP and Bill will do that as service now. You know, people say that these SaaS platforms are going to be disrupted. I actually think the opposite, that they're sitting on a gold mine, that they're going to be this flourishing of agents that are going to be specialized in Salesforce, specialized in, you know, well, Salesforce, I think they call Lightning and SAP as a BAP and everybody's got their own language, is that right? And we got Kuda and we've got Open USDA for Omniverse and who. Who's going to create an AI agent that's awesome at OpenUSD? We are, you know, because nobody cares about them more than we do. And so I think in a lot of ways these platforms are going to be flourishing with agents

and we're going to introduce them to each other and they're going to collaborate and solve problems. You see a wealth of different people working in every domain in AI. What do you think is undernoticed or that people, that you want more entrepreneurs or engineers or business people to go work on? Well, first of all, I think what is misunderstood and misunderstood maybe underestimated is the under the under the water activity, under the surface activity of groundbreaking science, computer science, two science and engineering that is being affected by AI and machine learning. I think you just can't walk into a science department anywhere, theoretical math department anywhere, where AI and machine learning and the type of work that we're talking about today

is going to transform tomorrow. If you take all of the engineers in the world, all of the scientists in the world, and you say that the way they're working today is early indication of the future, because obviously it is, then you're going to see a tidal wave of generative AI, A tidal wave of AI, a tidal way of machine learning, change everything that we do in some short period of time.

Now, remember, I saw the early indications of computer vision and to work with Alex and Ilya and Hinton in Toronto and, of course, Andrew Ang here in Stanford. And, you know, I saw the early indications of it, and we were fortunate to have extrapolated from what was observed to be detecting cats into a profound change in computer science in computing altogether. And that extrapolation was fortunate for us. And now, of course, we were so excited by, so inspired by it, that we changed everything about how we did things. But that took how long? It took literally six years from observing that toy, AlexNet, which I think by today's standards will be considered a toy, to superhuman levels of capabilities in object recognition. Well, that was only a few years.

What is happening right now, the ground swell in all of the fields of science, not one field of science left behind. I mean, just to be very clear. Everything from quantum computing, quantum chemistry, you know, every field of science, is involved in the approaches that we're talking about. If we give ourselves, and they've been added for a couple, two, three years. If we give ourselves a couple, two, three years, the world's going to change. There's not going to be one paper.

There's not going to be one breakthrough in science, one breakthrough in engineering, where generative AI isn't at the foundation of it. I'm fairly certain of it now. And so I think, I think, you know, there's a lot of questions about, you know, every so often I hear about whether this is a fad. computer you just got to go back to first principles and observe what is actually happening the computing stack the way we do computing has changed if the way you write software has changed i mean that is pretty core software is how humans encode knowledge this is how we encode

our you know our algorithms we encode it in a very different way now that's going to affect everything nothing else would be the same and so i i think the the uh I think I'm talking to the converted here, and we all see the same thing in all the startups that, you know, you guys work with and the scientists I work with and the engineers I work with, nothing will be left behind. I mean, we're going to take everybody with us. I think one of the most exciting things coming from the computer science world and looking at all these other fields of science is, like, I can go to a robotics conference now, a material science conference, a biotech conference, and I'm like, oh, I understand this. You know, not at every level of the science, but in the driving of discovery, it is all the algorithms that are general. And there's some universal, some universal unifying concepts. Yeah.

Yeah. And I think that's, like, incredibly exciting when you see how effective it is in every domain. Yep, absolutely. Yeah. And I'm so excited that I'm using it myself every day. You know, I don't know about you guys, but it's my tutor now. I mean, I don't do, I don't learn anything without first going to a way. AI. You know, why learn the hard way? Just go directly to an AI. I go directly to chat GPT or, you know,

sometimes I do perplexity just depending on just the formulation of my questions. And I just start learning from there. And then you can always fork off and go deeper if you like. But holy cow, it's just incredible. And almost everything I know, I double check. Even though I know it to be a fact, you know, what are I considered to be ground truth? I'm the expert. I'll still go to AI and check, Let me double check. Yeah, it's so great. Almost everything I do, I involve it, you know. I think it's a great note to stop on.

Thanks so much that time today. Yeah, I really enjoyed it. Nice to see you guys. Thanks, Jensen. Find us on Twitter at No Pryor's Pod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week.

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