Transcription
and you ask them, "Is AI likely to do more good than harm?" They're going to say, in their case, 80% would say AI will do more good than harm. In our case, it'd be the other way around. And so that tells you something that's very, very important. Socially. Socially, we need to be careful not to describe AI in these science fiction movie ways of describing AI and and causing people so much concern. Um, we want to be concerned, but we also want to be practical.
>> Guys, Jensen Huang just gave a pretty mind-blowing interview on how AI is going to change everything within the next 18 months. I think this is a must-watch interview for anyone who thinks that their job is safe. I mean, of course, Jensen won't directly say that AI will eat all your jobs, but the capabilities he defines that AI is going to achieve very soon are kind of horrible to even think of. I'm going to roll some clips and I'll share my thoughts as we go.
>> Now, the way that that AI works and and uh our technology works is that in the final analysis, the technology platform is built in layers. And that's one of the reasons why we think of it as a platform. You're standing on top of it. An application or an industry stands on top of that platform. That platform starts with energy on the bottom. One of the reasons why why uh this administration uh has made such a huge difference right away if it's is this pro-energy growth initiative its attitude about energy is that if we don't have energy we can't enable this new industry to thrive. It is absolutely true. So layer one is energy. Layer two are essentially the chips and systems, but the chips. That's where Nvidia comes in. Layer three is a whole bunch of software and we build a whole bunch of software on top of our chips and we're well known for this piece of software called CUDA. But there's hundreds of different pieces of software that we create that enables people to do AI for different fields of science or language or images or whatever it happens to be, robotics or manufacturing and such.
>> But that third layer is called infrastructure. Basically software. Now people historically have thought of infrastructure really as cloud but increasingly it's really important to realize that infrastructure includes land power shell because these are and I'll speak about this in a second that this industry spawned another industry altogether and I'll come back to that but the third layer is basically infrastructure and that infrastructure includes financial services because it takes an enormous amount of capital to do what we do. And historically all of that software the layer above that and this is where people largely focus on when they talk about AI which is the AI models. This is the revolutionary of course Chad GPT uh incredible work that anthropic does with cloud and uh Google does with Gemini and uh what XAI does with Grock. But the important thing to realize those are four of the 1 and a half million AI models in the world.
>> AI is not just intelligence that understands English or language, but it's AI that understands genes, proteins, chemicals, the laws of physics. AI that understands quantum. AI that understands physical articulation, otherwise known as robotics. AI that understands patterns across long sequence of time, financial services.
>> AI that understands longitudinally across multiple modalities, healthcare. And so AI has all of these different reaches and domains. We talk about this one area. We just have to be very careful to understand that AI AC spans basically every form of information across every field of science across every single industry. One and a half million AIs around the world. On top of that are all the applications and we never should forget that in the final analysis these AI models are technologies but technologies are about enabling application and use whether you're in healthcare you know you could be in entertainment manufacturing self-driving cars transportation each one of these industries have AIs that deeply affect them and these are the five layer stack Nvidia is at the lower level, the platform level. The reason why we say that, you know, we're AI company that works with every single AI company in the world is because we're the platform by which we are able to work with all of these technology companies and all these application companies across all these industries. And so that is a platform that we created. The mode that people describe, not so much a mode, but basically the the language by which all of these different applications and these different technologies speak to us is an architecture we invented 25 years ago called CUDA. And on top of that, CUDA libraries, a whole bunch of algorithms that we invented over the years. And that is basically Nvidia's platform. In the end, we don't build self-driving cars, but we work with every single self-driving car company in the world. In the end, we don't discover drugs, but we just have we work with every single drug discovery company in the world. We're the platform by which they build their things. We're a platform company.
>> So, in the following part, Jensen talks about how China is going to beat the United States in the AI race and how that's going to change the world order. He basically gave a warning to the West. This is a pretty interesting take and it really reveals how much behind we are compared to China in certain areas.
>> Okay. So, let me ask you. You said something um recently that was quite provocative. You you said that China was winning the AI race, the AI competition. Um um I know that you've got a powerful, you know, competitor in Huawei and Huawei has a lot of advantages you don't have. Why don't you describe this competition? Are we really losing?
>> It was a very good headline.
>> It was a great headline. Yeah.
>> And and u apparently caught up a lot of attention. uh uh the the as you know with headlines the disclaimer part uh the foundation part was left out of the headline but but the the way to think about that is let me just handicap it right now if you look at AI and go back to the first thing that we said AI is a five layer cake let's just always simplify it's not it's not quite this simplistic but let's simplify AI into a five layer cake energy chips infrastructure models and applications. Okay, I just and let's handicap it across the from top from bottom to top at the lowest level energy. China has twice the amount of energy we have as a nation.
>> I want to ask about that.
>> Twice as much energy as we have as a nation and there our economy is larger than theirs. Makes no sense to me. We also know that one of the most one of the most important initiatives, one of the most important policies of this administration and that was the first thing that President Trump said to me when we met is listen, we need to re-industrialize America. We need to unshore manufacturing again. We need to make we need to help America make things again. It's going to create jobs. That part of the economy has been outshored on you offshored uh and completely gutted the United States. We need to bring that back and he needs my help to do so. And so so that entire sector of the economy is missing. And however without energy, how do we build chip plants, computer system plants and these AI data centers? We call them AI factories. We're building simultaneously three different types of factories in the United States. Chip factories, supercomputer factories, and AI factories. They all require energy. Every single one of them. And so on the one hand, we want to re-industrialize the United States. How do you do that without energy? And so the fact that we vilified energy for so long, President Trump sticking his neck out and making taking it on the chin and helping this helping the country realize that energy is necessary for our growth is one of the the really one of the greatest things he's done right off the bat. And so now at the energy level, back to that stack, we're, you know, 50%. And they're growing straight up. We're kind of flat right now. And so number one, uh, energy. Number two, chips. We're generations ahead. We are generations ahead on chips. And I think everybody recognizes that. Number three, infrastructure. If you want to build a data center here in the United States, from breaking ground to standing up a AI supercomputer is probably about three years. They can build a hospital in a weekend. That's a real challenge. And so at the infrastructure le layer, their velocity of building things because they are builders. Their velocity of building things is extraordinarily high. Now, really quickly on on chips, we're several generations ahead. But don't be complacent. Remember, semiconductors is a manufacturing process. Anybody who thinks China can't manufacture is missing a big idea.
>> Okay. Next, Jensen talks about how nextg AI is going to be about efficiency and how China is catching up on that. He talks about how open source is important for AI to grow in the United States and how we're making a major mistake compared to China.
>> The large the language the model layer the model layer United States frontier models United are our frontier models are unquestionably world class. We are probably call it six months ahead. However, out of the 1.4 million models, most of them are open source. China is well ahead, way ahead on open source. Now, the reason why open source is so important is because without open source, startups can't thrive. University researchers can't do research. You can't teach AI. Scientists can't use AI. Basically, all of the industry around the your economy have no ability to fundamentally advance themselves unless you have open source. Without Linux, where would we be? Without Kubernetes, you know, without PyTorch, all of these different types of technologies that made AI thrive are all open source.
>> They are well ahead of us on open source.
>> And then the layer above that, applications. If you were to do a poll of of um uh their society and ours and you ask them, is AI likely to do more good than harm? They're going to say in their case, 80% would say AI will do more good than harm. In our case, it'd be the other way around. And so that tells you something that's very, very important. Socially. Socially, we need to be careful not to describe AI in these science fiction movie ways of describing AI and and causing people so much concern. Um, we want to be concerned, but we also want to be practical. AI is about automation and that area. I think that we need to be careful not to fall behind in the application and the diffusion of AI because in the end whoever applies the technology first and most wins that industrial revolution.
>> Now the following part is pretty interesting. Jensen's asked about robots, how robots are going to change our world. And his response reflects that robots are going to be our replacement. To be honest, that's a horrifying situation for humans. Two million robots, half of them were in China, which is really astounding when you think about it. Tell me how robots fit in with AI. Um, you know, let me just give you one example of why it's around the corner. You know, these days you could describe you could describe in text and you give it to um a video AI and it generates a video. You guys know this, right? It actually from words you can generate a video. Okay. And let's say the video is uh Jensen reaches over, picks up a cup. So, I take a picture of this screenshot, give it to the AI. That's the starting starting condition. And I say, "Now cause Jensen to reach over and pick up the cup." The AI creates pixel by pixel, token by token, my arm picking up the cup. And that everybody knows is possible today. You guys have seen it. Well, the AI can't tell the difference between it manu manipulating pixels versus it's manipulating a bunch of motors. So, the idea that I can tell the robot pick up the cup is clearly just around the corner. We just have to take that AI which currently sits in the cloud and we have to put it into otherwise called embody it into a physical >> mechanical system which is called robotics. So the AI is around the corner. We can see early evidence that the technology must be possible. Now China is going to be very very good at this for several reasons. They have great demand. They have a natural indigenous demand for more workers. Manufacturing is core part of what they do. We, by the way, because we're now re-industrializing, reshoring manufacturing, we now again also have significant demand for factory automation. And there's no question we have a shortage of labor. We have, right, we all know that our industries will be would be larger, more profitable, more vibrant if we just had more workers.
>> And so they have the same challenge. They have worker shortage coming up, very severe worker shortage coming up. So they have a a national strategic imperative to make sure that robotics happens. Number one. Number two, they have the AI technology. And number three, this is where they have a big advantage. They're really very good at electronics and mechanical intersections otherwise known as mechatronics. This entire area is they have the harmony of demand and supply side capability.
>> Now many other countries Japan has surely demand side they have the megatronics but Japan needs to have much better AI technology. Germany great demand extraordinary mechatronics they need to have great AI technology. United States we have if we reshort industrial indust re-industrialize our nation we will have great demand we have great sa software technology but we really at the moment need to improve our mechanical electronic skill.
>> All right guys that's it for today's video I'll see you again next week with another.