Jensen Huang and Arthur Mensch on Winning the Global AI Race

26:03The other thing about technology is when it becomes faster, it's easier. Could you imagine back in the old days, of course, I had the benefit of seeing computers from its earliest days and the performance of the computers were so frustratingly slow everything you did was hard. But these days, the type of things we do is just magical because it's also fast. And so whether it's motivated by your institutional need to engage the most consequential technology of all time or the fact that it's getting better all the time. So it's not that hard. I think the number of excuses is running out. So let's talk about that for a second, because change is hard.

26:43I've got an endless list. If I'm a nation state leader, I'm facing increasing amounts of geopolitical risk. I don't know who my allies are. Elections are coming. There's any number of things I've got to deal with. But now let's say I understand that this is important. You guys spend so much time talking to nation state leaders who are thinking about what are the risks of adopting AI too fast? And you're right. The zeitgeist has shifted based on the Paris action summit. it seemed like there's a tone of optimism more than there was a tone of pessimism a year ago. But what are the most common questions you get from nation -state leaders when they're asking about risks and how to think about them?

27:17So I've heard several questions, but one of the risks is to see your population start jetting afraid of the technology for fear of it replacing them. And that is something that can actually be prevented. We collectively make sure that everybody get access to the technology and is trained in using it. So the scaling of the various citizens of the populations is extremely important and stating AI as an opportunity for them to actually work better and showing the purpose of it through applications, through things that they can actually install on their smartphone through public services. We're working, for instance, with the French unemployment system to actually connect opportunities of jobs to an economic people through AI agents.

28:01that are being actionated by human operators within the agency. And that is one opportunity. That's a very palatable opportunity for people to find a job better. And so that's part of the thing that can make sure that population understand the opportunity and the fact that AI is really just a new change for them to adopt. Just the same way they had to adopt personal computers in the 90s and internet in the 2000s. The common aspect with these changes is that you need people to to embrace the technology. And I think the biggest problem that nation states may have is to see AI increase the digital divide.

28:40That is already relatively big. But if you work together, and if done in the right way, we can make sure that AI is actually reducing the digital divide. AI is a new way to program a computer. It is because by typing in some words, you can make the computer do something, just like we did in the past. And now you talk to it. You can interact with it in a whole lot of ways. You can make the computer do things for you a lot easier today than it was before. Right. The number of people who could prompt chat GPT and do productive things. Right. Just from a human potential perspective is vastly greater than the number of people who can program C++ ever.

29:25And therefore we have closed the technology divide. Probably the greatest equalizer we've seen. It is by definition the greatest equalizer of technologies of all time. But you still need to have citizens to know about it. I think that's the thing. I'm just describing the fact. The fact is there are more people who program computers using Chatchy PT today than there are people who program computers using C++. That's a fact. And so the fact is this is the greatest force reducing the technology divide the world's ever known. It's just perceived, and what Arthur's saying, the perception through, I don't know who, and I'm talking about it, and I don't know how, talking about it, but the fact that a matter is, it is not stopping.

30:15It's not stopping anything. The number of people who are actively using Cheshire PT today is off the charts. I think it's terrific. It's completely terrific. anything else apparently isn't working. And so I think people realize the incredible capabilities of AI and how it's helping them with their work. I use it every single day. I used it this morning. And so every single day I use it. And I think that deep research is incredible. My goodness, the work that Arthur and all of the computer scientists around the world are doing is incredible. And people know it. People are picking it up obviously, right?

30:51Just a number of active people who are doing users. Let's talk about open source for a bit because both of you have talked quite publicly about the importance of open models in the context of sovereignty. I had deep mind, part of the Chinchilla skating laws which were openly published, your co -founder Guillaume created Lama. And then last year, Nvidia and Mistral worked on a jointly trained model called Mistral Nemo. Why are open models such a big part of your focus? Because it's a norisontel technology and enterprises and States are going to be eventually willing to deploy it on their own infrastructure.

31:24Having this openness is important from a sovereign perspective. That's the first point. And then the second point of importance is that releasing open -source models is a way to accelerate progress. And we created Mistral on the basis that what we've seen during our early career when and we were doing AI in between 2010 and 2020 was an acceleration of progress because every lab was building on top of each other. And that's something that kind of disappeared with the first large language models from OpenAI in particular. And so spinning back that OpenFlare Wheel of I contribute something and then another lab is contributing something else.

32:04And then we iterate from that is the reason why we created Mistral. And I think we did a good job at it because we started to release models and then Meta started to release models as well. And then we had Chinese company, like DeepSeek, release stronger models, and everybody benefit from it. Coming back to Mral Nemo, one difficulty of trading AI models in an open way is that this is more a cathedral than a bazaar setting when it comes to open source. Because you have large spend to do to build a model. And so what we did with NVIDIA team is really to mix the two teams together, have them work on the same infrastructure, the same code, have the same problems and combine their expertise to build the same model.

32:43And that has been very successful because Nvidia brought a lot of things we didn't know. I think we brought things that Nvidia didn't know. And at the end of the day, we produced something that was at the time the best model for its size. And so we really believe in such collaborations. And we think that we should do them more and at a higher scale. And not only with only two companies, but probably with three or four. And that's the way open source is going to prevail. I completely agree. the benefit of open source in addition to accelerating and elevating the basic science, the basic endeavor of all of the general models and the general capabilities is the open source versions also activate a ton of niche markets and niche innovation.

33:31All of a sudden health care, life sciences, physical sciences, robotics, transportation, the number of industries that were activated as a result of open source capabilities that are sufficiently good is incredible. Don't ignore the incredible capabilities of a open source, particularly in the fringe, the niche. But mission critical where data might be sensitive. Yeah, it could be, for example, in mining energy. Right. Who's going to go create an AI company to go mine energy? Energy is really important. but the mining of energy is not that big of a market. And so open source activates every single one of them.

34:05Financial services, it turns out, activates them. Healthcare, defense. You pick your favorites. Anything that is mission critical and that requires to do one's own deployment that potentially requires to do on the edge deployment as well. And anything that requires some stronger editing and the ability to do a thorough evaluation of it, you can evaluate a model much better if you have access to the weights, then if you only have access to APIs. And so if you want to build certainty around the fact that your system is going to be 100 % accurate, I don't think you should be using your cross -source model.

34:40And you have to connect it into your flywheel. How are you going to connect your local data? Yeah, the connected into your local data, your own local experience. The more you use it, the better it becomes that flywheel. You can't do it without open source. But let's say I'm a nation -state leader. I've been considering open source. I'm trying to hear things like, hey, open sources is a threat to national security. We should not be exporting our models because these open models actually give away a ton of nation state secrets, or more importantly, the bad guys can use these open models too. And so this is a threat to security.

35:13Instead, what we should be doing is locking down maybe development amongst two or three labs that have the infrastructure to get licenses from the government to do training, to do the right safety and certification. I've certainly been hearing that a lot. How should I think about that versus what you're telling me, which is actually an open as better for mission -critical industries? Collaboration in between labs is going to be critical for humanity success. And if one state decides to lock things down, the only thing that is going to happen is that another state will take the leadership. Because cutting yourself from the open flywheel is just too high of a cost for you to maintain competitiveness.

35:48If you do that, this is a debate that has occurred in the United States. And effectively, if there's some export control over weight. This is not going to stop any country, any Europe, any country, in Asia to continue its progress. And they will collaborate to actually accelerate that progress. So I think we just need to embrace the fact that this is an horizontal technology very similar to programming languages. Programming languages are all open -source, right? So I think AI just needs to be open -source in that respect. We're glad to see that this realization that we could accelerate together by being more open about the way we build the technology.

36:25And so it's great to see that open source has a lot of good days before it. It is impossible to control. Software is impossible to control. If you want to control it, then somebody else's will emerge and become the standard. Just as Arthur mentioned. And question is, is open source safer? for open source enables more transparency, more researchers, more people to scrutinize the work. The reason why every single company in the world is built, every cloud service provider is built on open source is because it is the safest technology of all. Give me an example of a public cloud today that's built on an infrastructure stack that isn't open source.

37:15You start from open source, you could customize it. Right. But the benefit of open source is the contribution of so many people and the scrutiny. Very importantly, you can't just put any random stuff into open source. You get laughed off the internet. You've got to put good stuff on the open source because the scrutiny is intense. So I think open source provides all of that. Great collaboration to accelerate innovation, escalate excellence, insure transparency, see attract scrutiny, all of that improves safety. In a sense, you're saying it's partly more secure because as we've seen with open source databases, storage, networking, compute, you get mass red teaming.

37:59The whole world can help you red team your technology versus just a small group of researchers inside your company. Is that roughly right? Yeah, exactly. Yeah, by pooling a lot of organizations together to come up with a technology that they can and all use and specialize on their own domains. You're forcing the technology to be good for every one of them. And so that means you're removing biases. You're really making sure that the general purpose models that you're building are as good as possible and don't have failures. And I think open source in that respect is also ways to reduce the number of failure points.

38:32If as a company today, I decide to rely fully on a single organization and on its safety principles, on this red teaming organization as well, trusting it a little too much. Whereas if I'm building my technology on open source models, I'm trusting the world to make sure that the basis on which I'm building is secure. So that's a reduction of failure points. And that's obviously something that you need to do as an enterprise or as a country. We're going to transition a little bit now into company building, which is something a lot of people are excited to hear from both of you about. So let's start with your gents.

39:06And you've remarked that Nvidia is the smallest big company the world. What enabled you to operate that way? Our architecture was designed for several things. It was designed to adapt well in a world of change, either caused by us or affecting us. And the reason for that is because technology changes fast. And if you over -correct on controllability, then you are under serving a system's ability to become agile and to adapt. And so our company uses words like aligned instead of used words like control. I don't know that one time I've used a word control in talking about the way that the company works.

39:52We care about minimum bureaucracy and we want to make our processes as lightweight as possible. Now, all of that is so that we can enhance efficiency, enhance agility, and so on and so forth. We avoid words like division. When Enviable was first started, it was modern to talk about divisions. And I hate the word divide. Why would you create an organization that's fundamentally divided? I hated the word business units. The reason for that is because why should anybody exist as one? Why don't you leverage as much of the company's resources as possible? I wanted a system that was organized much more like a computing unit, like a computer to deliver on an output as efficiently as possible.

40:43And so the company's organization looks a little bit like a computing stack. and what is this mechanism that we're trying to create? And in what environment are we trying to survive in? Is this much more like a peaceful countryside or is this like much more like a concrete jungle? What kind of environment are you in because the type of system you wanna create should be consistent with that. And the thing that always strikes me odd is that every company's org chart looks very similar but they're all different things. ones are snake, the other ones are elephant, the other ones are a cheetah, and everybody is supposed to be somewhat different in that forest, but somehow they all get along.

41:25Same exact structure, same exact organization doesn't seem to make sense to me. I agree that it feels like companies have personalities, and despite the fact that they're organized, sometimes similarly. I should say that obviously we have a lot of things to learn, and I mean the company is not even two years old. And I guess when challenge we have with Ms. Trial and I think our competitors have the same challenge is that this is the one of the first time that the software company is actually a deep tech company that is driven by science. Science doesn't have the same time scales as software. You need to operate on a monthly basis.

42:00Sometimes you don't know exactly when the thing will be ready. But on the other hand, you have customers asking when is the next model coming up? When is the scalability going to be available? and so you need to manage expectation. I think for us, the biggest challenge, and I think we're starting to do a good job at it, is to manage the hinge in between the product requirements and what the science is able to do. Research and product. Research and product. And you don't want the research team to be fully dedicated to making the product work. So you need to work, and I think we've started to do it on making sure that you have several frequencies frequencies in your company.

42:40You have fast frequencies on the product side, iterating every week. And you have slow frequencies on the science side that are looking at why profoundly the product is failing on certain domains and how they could fix it through research, through new data, through new architecture, through new paradigm. And I think that's fairly new. This is not something that you would find in the typical SaaS company because this is inherently a science problem. I mean, Nvidia is one of the most successful companies that have over a 30 -year timeline has figured out a way to keep science and research ahead of the rest of the world, whether it was CUDA back in 2012, where those fundamental systems research, or Cosmos today, which is now saying, you know, it's definitely state -of -the -art on how simulations should work out.

43:25We've harmonized exactly what Arthur just said. Is that a heuristic right for you? Yeah, we harmonized that inside our company. We have basic research, applied research, and then we have architecture, and then we have product development. And we have multiple layers of it. And these layers are all essential. And they all have their own time clock. In the case of basic research, the frequency could be quite low. On the other hand, all the way to the product side, we have a whole industry of customers who are counting on us. And so we have to be very precise. And somewhere between basic research and discovering hopefully surprises that nobody expects on the one hand, on the other hand, to be able to deliver on what everyone expects.

44:07Vardictively. Okay. These two extremes, we manage harmoniously inside our company. There's so many fascinating things about this market, but there's one in particular that I want to call out. Both of you have customers that are also your competitors. And those competitors are huge and highly capitalized tech trends and video sales GPUs to AWS, which is building its own chips called Trainiam. And Arthur, you're training models that you sell through AWS and Azure who have funded labs like Anthropic and OpenAI. So how do you win an environment like this and how do you manage those relationships? Because we talked about company building internally, but now I'm curious externally, how do you survive?

44:43Jensen said it well. You give up control but you work on alignment. And despite the fact that sometimes you have certain companies can be competitors, you You may have a line of interest and you can work on specific agendas that are shared. You have to have your own place. Obviously, these cloud service providers aren't working with Arthur because they already have the same thing. They just want two of the same things. It's because Arthur and Mistral has a position in the world that is unique to Mistral. They add value in a particular place that is unique. A lot of the conversation we've had today are areas that Mistral and the world have work and their position in the world, make some uniquely good at.

45:25And we are different. We're not just another ASIC. We can do things for the CSPs and do things with the CSPs that are not possible for them to do themselves. For example, Nvidia's architecture is in every cloud. And in a lot of ways, we have their first onboarding for amazing future startups. And the reason for that is because by onboarding to Nvidia, they don't have to make a strategic or business or otherwise commitment to a major cloud. They could go into every cloud and they could even decide to build their own system they like because the economics turns out to be better for them at some point or they would like access to capabilities that we have that are somewhat protective within the clouds.

46:09And so whatever the reasons are in order to be a good partner to somebody, you still have to have a unique position. You need to have a unique offering. And I think, Mr. Asa, very unique offering. We have a very unique offering. And our position in the world is important to even the people we compete against. And so I think when we are comfortable within that and comfortable with our own skin, then we can be excellent partners to all of the CSPs. And we want to see them succeed. I know that it's a weird thing to say when you see them as a competitor, which is a reason we don't see them as a competitor.

46:42We see them as a collaborator who happens to compete with us as well. And probably the single most important thing that we do for all the CSPs is bring them business. And that's what a great computing platform does. We bring people business. I remember when Arthur and I first met, we sat down in London at a late night restaurant and sketched out the plan for his Series A. And we were figuring out why he needed so much capital for the Series A, which in hindsight was remarkably efficient. I think the Mistral Series A we put together was half a billion relative to other folks who had to spend multiple billions to get to the same place.

47:13But I asked him, what chips would you like to run on? And you looked at me so absurdly as if I had asked you a question that, how could it be an answer other than Nvidia, other than H100? And I think that ecosystem has been the startup ecosystem that Nvidia has invested in, creates so much business for the clouds. What is the philosophy that led you to invest so deeply in startups and founders? So early on, even before anybody knew about them? Two reasons I would say one, the first reason is I rarely call this as a GPU company. What we make is a GPU, but I think of NVIDIA as a computing company.

47:48If you're a computing company, the most important thing you think about is developers. If you're a chip company, the most important thing you think about is a chip. And all of our strategies, all of our actions, all of our priorities, all of our focus, all of our investments, 100 % of it is aligned with the attitude that is developer first. It's about the computing platform first another way of saying ecosystem right and so everything starts there Everything ends there GTC is a developer's conference right all of our initiatives inside the company is a developer first So that's number one the second thing is we were pioneering a new computing approach that was very alien to the world of general purpose computing and so this accelerated computing approach was rather alien and counterintuitive and rather awkward for a very long time.

48:39And so we're constantly seeking out, looking for the next incredible breakthrough, the next impossible thing to do without accelerated computing. And so it's very natural that I would find and would seek out researchers and great thinkers like Arthur, because you know, I'm looking for the next killer app. And so that's kind of a natural intuition, natural instinct of somebody who's creating something new. And so if there's an amazing computer science thinker that we haven't engaged with, that's my bad. We got to get on it. That's a perfect segway from a computing perspective. One of the most significant trends you see on the horizon.

49:16And in particular, for an audience who might be prime ministers of presidents or ministers of IT and some of the world's fastest growing markets trying to understand where computing is going, how would you guide them? We are moving towards workloads that are more and more asynchronous. So workloads where you give a task to an AI system and then you wait for it to do 20 minutes of research before returning. So that's definitely changing a bit the way you should be looking at a structure because that creates more load. So I guess it's a good case for data centers and for Nvidia. As I've said, I guess in the beginning of this episode, all of this is not going to happen well if you don't have the right onboarding infrastructure for the agents.

49:54If you don't have a proper way for your AI systems to learn about the people they are interact with and to learn from the people they interact with. So that aspect of learning from human interaction is going to be extremely important in the coming years. And there's another aspect which is around personalization of having, I guess, models and systems consolidate the representation of their users to be as useful as possible. I think we are in the early stage of that. That's going to change, again, pretty profoundly, the interaction we have with machines that will know more about us and know more about our tastes than how to be as useful as possible to others.

50:33As a leader of a country, I want to think about education, about making sure that I have a local talent pool that understands AI enough to create specialized AI systems. And I want to think about infrastructure both on the physical side, but also on the software side. So what are the right primitives? What is the right partner to work with? That is going to provide you with the platform of unboarding. And so those two things are important. If you have this and you have the talent, and if you do deep partnerships, the economy of your state is going to be profoundly changed. The last 10 years, we've seen extraordinary change in computing.

51:09From hand -coding to machine learning, from CPUs, the GPUs, from software to AI across the entire stack, the entire industry has been completely transformed. And we're going through that still. The next 10 years is going to be incredible. Of course, the industry has been wrapped up in talking about scaling laws. And pre -training is important, of course, and continues to be. Now we have post -training. And post -training is thought experiments and practice and tutoring and coaching. and all of the skills that we use as humans to learn the idea that thinking and agentic and robotic systems are now just around the corner is really quite exciting.

51:53And so what it means to computing is very profound. People are surprised that Blackwild is such a great leap over Hopper. And the reason for that is because we built Blackwild for inference and just in time because all of a sudden thinking is such a big computing load. And so that's one layer, is there's a computing layer. The next layer is the type of AI's that we're going to see. There's the agent tech AI, the informational digital worker AI's, but we now have physics AI that's making great progress. And then there's physical AI that's making great progress. And physics AI is, of course, things that obeyed the physical laws and the atomic laws and the chemical laws and all of the various physical science, there's that we're going to see some great breakthroughs.

52:37and I'm very excited about that, that affects industry, that affects science, affects higher education and research, and then physical AI, AI that understand the nature of the physical world, from friction to inertia, the cause and effect, object permanence, those kind of basic things that humans have common sense, but most AI's don't. And so I think that's gonna enable a whole bunch of robotic systems that are gonna have great implications and manufacturing in others. the US economy is very heavily weighted on knowledge workers. And yet many of the other countries are very heavily weighted on manufacturing.

53:14And so I think for many of the prime ministers and the leaders of countries to realize that the AIs that they need to transform and to revolutionize their industries that are so vital to them whether its energy focused or manufacturing focused is just around the corner and they ought to stay very alert to this. I would encourage people not to over -respect the technology. And sometimes when you over -admire a technology, over -respect the technology, you don't end up engaging it. You're afraid of it somehow. Some of the things that we said today about AI closing the technology divide is really something that I'd be recognized.

53:54This is of such incredible national interest that you have the responsibility to engage it. And you know, exciting times ahead. That was incredible. Thank you both so much for making time. If they want to go learn more, they want to figure out how to partner with the Duke on college. Call us. You kidding me? You can call us. Yes. I'm going to do it. Yeah. Yeah. We'll start with listening to this podcast and then giving them a speed dial. We'll put their numbers in the show notes. Jensen and video .com. Job done. You heard it here. We'll very response you. I can attest to that. All right. Thank you so much.

54:26All right. Thank you. All right. Thank you. Alright, that is all for today. If you did make it this far, first of all, thank you. We put a lot of thought into each of these episodes whether it's guests, the calendar Tetris, the cycles with our amazing editor Tommy until the music is just right. So if you like what we put together, consider dropping us a line at ratethispodcast .com slash A16z. And let us know what your favorite episode is. It'll make my day and I'm sure Tommy's too. We'll catch you on the flip side.