Jensen Huang and Arthur Mensch on Winning the Global AI Race
Jensen Huang on The a16z Show.
Passages
Setup is always hard. This is no different. The only question is, do you need to do it? If you want to be part of the future, and this is the most consequential technology of all time, not just our time, of all time, digital intelligence, how much more valuable, how much more important can it be?
SPEAKER_03Check at the sourceBy the way, everything Arthur said is 100% correct. It is also exactly the reason why everybody's given up. And it's precisely wrong. And the reason for that is this. If it's a general purpose technology and one company can build the ultimate general purpose technology, then why shouldn't anybody else do it? And that is the flaw. But that's also the mind trick. To convince everyone that intelligence is only something that a few people ought to go build, everybody ought to sit back and wait for it. I would advise that everybody engage AI. And it is not just a few companies in the world who should build it. Everybody should build it. Nobody's going to care more about the Swedish culture and the Swedish language and the Swedish people and the Swedish ecosystem more than Sweden. Right. Nobody's going to care about the ecosystem of Saudi Arabia more than Saudi Arabia. And nobody's going to care about Israel more than Israel, despite the fact that the technology is general purpose and absolutely true. How could intelligence not be general purpose? It is also hyper-specialized. And the reason for that is because, let's face it, I don't think I'm waiting around for a general-purpose chatbot to be an expert in a particular area of disease. I still think that I would prefer to have somebody who is hyper-specialized in that field to fine-tune, to train, and post-train, if you will, an AI model that's going to be specialized in that.
SPEAKER_03Check at the sourceWould you agree with that, Jensen? A couple of ways to think about it. Your country's digital intelligence is not likely something you would want to outsource to a third party without some consideration. Your digital intelligence is just now a new infrastructure for you. Your telecommunications, your healthcare, your education, your highways, your electricity. This new layer is your digital intelligence. It's your responsibility to decide how you want this digital intelligence to evolve. And whether you want to outsource it so that you could never have to worry about intelligence again, or this is something that you feel you want to engage, maybe even control and shape into a national infrastructure. Of course, it has all the things that Arthur said, AI factories, infrastructure, et cetera. There's another way you could think about it is your digital workforce. Now this is a new layer, and you've got to decide whether the digital workforce of your country or your company is something that you decide to outsource, hope it evolves the way that you would like it to, or is it something that you want to engage, maybe even decide to control and nurture and make better? We hire general purpose employees all the time. We hire them out of school. Some of them are more general purpose than others. Some of them are more intelligent than others. But once they become our employees, we decide to onboard them, train them, guardrail them, evaluate them, continuously improve them. we make the investment necessary to make general purpose intelligence into super intelligence that we could benefit from. And so I think that that second layer, thinking about it as digital workforce, in both cases, It contributes to the national economy. In both cases, it contributes to social advance. In both cases, it contributes to the culture. And I think that in both cases, a country needs to play a very active role in it. And so I think it's back to your original question about sovereign AI, how to think about it. Yes, it is definitely a general purpose technology, but you have to decide how to shape it. Your country's digital data... belongs to you. Your national library, your history, for so long as you want to digitize it, you could make it available to everybody in the world. You could also make it available to companies or researchers and institutions in your own country. It belongs to you. Of course, these are all vaporous things. They're very soft ideas. But it does belong to you. And you could decide it belongs to you in the sense that this is where you came from. You could decide how to put it to use for the benefit of your people. And it belongs to you in the sense that it's your responsibility to shape its future. Sovereign AI. It's your responsibility.
SPEAKER_03Check at the sourceYour IT department is going to become the HR department of your digital workforce. And they're going to use these tools that Arthur describes to onboard AIs, fine-tune AIs, guardrail them, evaluate them, continuously improve them. And that flywheel will be managed by the modern version of the IT department. And we'll have biological workforce and we'll have a digital workforce. It's fantastic. And so nobody's going to do this for you. You've got to do it yourself. That's why even though we have so many technology companies in the world, every company still has their own IT department. I've got my own IT department. I'm not going to outsource it to somebody else. In the future, they'll be even more important to me because they'll be helping us manage these digital workforces. You're going to do this in every country. You're going to do this in every company within those countries. And so the space for what Arthur is describing to take this general purpose technology and But to really fine-tune it into domain experts, they're national experts or they're industrial experts or they're corporate experts or functional experts. This is the future, the giant future space of AI.
SPEAKER_03Check at the sourceYou said norms. That exactly means it's soft versus rules, which are more hard. Or algorithms and laws, which are very specific.
SPEAKER_03Check at the sourcePreference. Somebody's preference is multidimensional. What do you prefer? It depends. Well, there's so many features that defines my preference. It takes AI to be able to precisely comply with the description that Arthur was describing just now. Could you imagine if a human had to write this in Python? Describe every one of these, capture every one of these things in C++. Based on this, I prefer that. But if you did that, I prefer that other thing. And I mean, the number of rules would be insane. Which is the reason why AI has the ability to codify all of this. It's a new programming model that can deal with the ambiguity of life.
SPEAKER_03Check at the sourceCulture reflects your values. We were just talking about how each one of these AI models, AI services, respond differently to the type of questions you're asked. Because they've codified the values of their service or the values of their company into each one of their services. Could you imagine this now amplified at an international scale?
SPEAKER_03Check at the sourceSome of it is universal. For example, it is possible for certain companies to serve nations and society and companies around the world because it's basically universal. But it cannot be the only digital intelligence layer. It has to be augmented by something regional. You know, I think McDonald's is pretty good everywhere. All right. Kentucky Fried Chicken is pretty good everywhere. But you still want the local style, local taste that augments on top of that. The last mile. That's right. The local cafes, the mom and pop restaurants, because it defines the culture. Right. It defines society. It defines us. I think it's terrific that you have Walmart everywhere, that you can count on everywhere. I think it's fine. But you need to have local taste, local style, local preference, local excellence, local services. Let me swing it another way. It is very likely that in the context of our digital workforce in the future, we will have some digital workers which are generic. Right. They're just really good at doing maybe basic research or something basic. Or they're useful for every company. It's unnecessary for me to create something new. I think Excel is pretty good. Microsoft Office is universally excellent. Right. I'm perfectly fine with it. That's right. Right. Then there's industry specific tools. Right. Industry specific expertise that is really important. For example, we use Synopsys and Cadence. Arthur doesn't have to. Because it's specific to our industry, not his. We probably both use Excel. Probably both use PDFs. We both use browsers. And so there's some universal things that we can all take advantage of. And there'll be universal digital workers that we can take advantage of. And then there'll be industry specific. And then there'll be company specific. Inside our company, we have some special skills that are very important to us that defines us. It's highly biased, if you will. Very guardrail to doing very specific work, highly biased to the needs and the specialties of our company. And so we become superhuman in those areas. Well, your digital workforce is going to be the same and AI is going to be the same. There'll be some that you just take off the shelf. The new search will likely be some AI. The new research will probably be some AI. But then there'll be industrial versions of AIs that we'll maybe get from Cadence and others. And then we'll have to groom our own using Arthur's tools. And we'll have to fine-tune them. We'll onboard them. We'll make them incredible.
SPEAKER_03Check at the sourceYou have to get it in your head that it's not as hard as you think it is. First of all, because the technology is getting better, it's easier. Could you imagine doing this five years ago? It's impossible. Could you imagine doing this five years from now? It'll be trivial. And so we're somewhere in that middle. The only question is, do you have to do it? The truth of the matter is I hate onboarding employees. And the reason for that is because it takes a lot of work. But once you set up an HR organization and leadership mentoring organization and processes, then your ability to onboard employees is easier and is systematically more enjoyable for everybody involved. But in the very beginning, it's hard. Setup is always hard. This is no different. The only question is, do you need to do it? If you want to be part of the future, and this is the most consequential technology Of all time. Not just our time, of all time. Digital intelligence, how much more valuable, how much more important can it be? And so if you come to the conclusion this is important to you, then you have to engage it as soon as you can, learn along the way, and just know that it's getting easier and easier all the time. The fact of the matter is if we try to do agentic systems even three years ago, it was incredibly hard. But agentic systems are a lot easier today. And all of the tools necessary for curating data sets, for onboarding the digital employees, to evaluating the employees, to guardrailing digital employees, all of those are getting better all the time. The 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.
SPEAKER_03Check at the sourceAI 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. Right. I know you talked 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. The number of people who could prompt chat GPT and do productive things just from a human potential perspective is vastly greater than the number of people who can program C++ ever. And therefore, we have closed the technology divide. It is by definition the greatest equalizer of technologies of all time.
SPEAKER_03Check at the sourceThe fact is there are more people who program computers using ChatGPT today than there are people who program computers using C++. Right. That's a fact. And so the fact is, this is the greatest force of reducing the technology divide the world's ever known. Right. 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 of the matter is, it is not stopping. It's not stopping anything. The number of people who are actively using ChatGPT today is off the charts. I think it's terrific. It's completely terrific. Anybody who's talking about 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 the 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? Just the number of active users.
SPEAKER_03Check at the sourceI 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. All of a sudden, healthcare, 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 open source, particularly in the fringe, the niche. Yeah, it could be, for example, in mining energy. 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. Financial services, it turns out, activates them. You pick your favorites.
SPEAKER_03Check at the sourceAnd you have to connect it into your flywheel. How are you going to connect? Your local data. Yeah, you have to connect it 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.
SPEAKER_03Check at the sourceIt 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 the question is, is open source safer? 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. You start from open source. You could customize it. 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'll get laughed off the internet. You've got to put good stuff on open source. Because the scrutiny is intense. And so I think open source provides all of that. Great collaboration to accelerate innovation, escalate excellence, ensure transparency, attract scrutiny, all of that improves safety.
SPEAKER_03Check at the sourceOur 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 overcorrect on controllability, then you are underserving a system's ability to become agile and to adapt. And so our company uses words like aligned. instead of use words like control. I don't know that one time I've used the word control in talking about the way that the company works. We 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 NVIDIA was first started, it was modern to talk about divisions. And I hated 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. And 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 want to 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. One's a snake, the other one's a elephant, the other one's a cheetah, and everybody is supposed to be somewhat different in that forest, but somehow they all get along. Same exact structure, same exact organization doesn't seem to make sense to me.
SPEAKER_03Check at the sourceYeah, we harmonize 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. Right. On the one hand, on the other hand, to be able to deliver on what everyone expects. These two extremes, we manage harmoniously inside our company.
SPEAKER_03Check at the sourceYou 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. And 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 work and their position in the world makes them uniquely good at. And 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 are the 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. And 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 Mistral has a 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 the reason we don't see them as a competitor. We 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.
SPEAKER_03Check at the sourceTwo reasons, I would say. One, the first reason is I rarely call this a GPU company. What we make is a GPU, but I think of NVIDIA as a computing company. If you're a computing company, the most important thing you think about is developers. Right. 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. And so everything starts there. Everything ends there. GTC is a developer's conference. All of our initiatives inside the company is 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. Right. rather awkward for a very long time. And 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 is 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.
SPEAKER_03Check at the sourceThe last 10 years, we've seen extraordinary change in computing. From hand coding to machine learning, from CPUs to 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 things. The idea that thinking and agentic and robotic systems are now just around the corner is really quite exciting. And so what it means to computing is very profound. People are surprised that Blackwell is such a great leap over Hopper. And the reason for that is because we built Blackwell for inference purposes. 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 AIs that we're going to see. There's the agentic AI, the informational digital worker AIs. 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 obey the physical laws and the atomic laws and the chemical laws and all of the various physical sciences that we're going to see some great breakthroughs. And 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 AIs don't. And so I think that that's going to enable A whole bunch of robotic systems that are going to have great implications in manufacturing and others. The U.S. economy is very heavily weighted on knowledge workers. And yet many of the other countries are very heavily weighted on manufacturing. And 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 are That are so vital to them, whether it's energy focused or manufacturing focused, it's 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 ought to be recognized. This is of such incredible national interest that you have the responsibility to engage it. Anyhow, exciting times ahead.
SPEAKER_03Check at the sourceJensen at NVIDIA.com. Job done. You heard it here. We're very responsive.
SPEAKER_03Check at the source
Every passage above is taken from this recording: The a16z Show, Jensen Huang and Arthur Mensch on Winning the Global AI Race.