NVIDIA’s Jensen Huang on Reasoning Models, Robotics, and Refuting the “AI Bubble” Narrative

So with everything that's happened in 2025 and, you know, being in the middle of the vortex with it, what do you reflect on and say, like, this surprised you most or this is the biggest change? See, there's some things that didn't surprise me, like, for example, the scaling loss didn't surprise me because we already knew about that. The technology advancement in surprise me, I was pleased with the improvements of grounding. I was pleased with the improvements of reasoning. I was pleased with the connection of all of the models to search. I'm pleased that there are now routers that are in front of these models so that it could, depending on the confidence of the answers, go off and do necessary research and just generally improve the quality and the accuracy of answers. I'm hugely proud of that. I think the whole industry addressed one of the biggest skeptical responses of AI, which is hallucination and generating gibberish and all of that stuff. I thought that this year, the whole industry, everything from every field, from language to vision, to robotics, to self-driving cars, the application of reasoning and the grounding of the answers, big, big leaps. Would you guys say this year? Things like open evidence to for medical information, where doctors are not really using that as a trusted resource, like Harvey for legal, you're really starting to see AI emerge as one of these things become a trusted tool or a counterparty for experts to actually be able to do it. They do much better. That's right. In a lot of ways, I was expecting it, but I'm still pleased by it. I'm proud of it. I'm proud of all of the industry's work in this area. I'm really pleased and probably a little bit surprised, in fact, that token generation rate for inference, especially reasoning tokens, are growing so fast, several exponentials at the same time as it seems, and I'm so pleased that these tokens are now profitable, that people are generating, I heard today that open evidence, speaking of them, 90% gross margins. Those are very profitable tokens, and so they're obviously doing very profitable or very valuable work. Cursor, their margins are great. Clots margins are great. For the enterprise use of opening, their margins are great. In other words, it's really terrific to see that we're now generating tokens that are sufficiently good, so good in value that people are willing to pay good money for. I think these are a really great grounding for the year. Some of the things that the narrative, of course, the conversation with China really occupied a lot of my time this year, geopolitics, the importance of technology in each one of the countries. I spent more time traveling around the world this year than just by any time the hit. All of my life combined. My average elevation this year is probably about 17,000 feet. It's nice to be here on the ground with you guys. So I think geopolitics, the importance of AI to all the nations, all worth talking about later. Of course, I spend a lot of time on expert control and making sure there are strategies nuanced and really grounded and promotes national security. But recognizing the importance of various facets of national security, a lot of conversations about that. You know, of course, lots of conversation about jobs, the impact of AI, energy, labor shortage. I mean, boy, we covered everything that we know. Everything was AI. Everything was AI. Yeah. It was incredible. It was definitely the center of the storm for like every one of those themes. Maybe when we can start with actually these jobs, because they're jobs in employment, because when I look at the traditional AI community, even before things were scaling and even before AI was really working, there was a strong sort of James Day component in the people working on AI, oddly enough, right? The people who were most trying to push the field forward were often the people who were most pessimistic, which is very odd. Why would you do that? That was. And I feel like that narrative is taken over some subset of media or some set of other things, despite all the things that we think are very positive about what AI has done. That's going to help with health care, with education, with productivity, with all these other areas. And in general, whenever we have a technology shift, you have a shift in terms of the jobs that are important, but you still have more jobs. That's right. Could you talk about how you think about employment and jobs and sort of what people are saying and what you think the real narrative is there? Maybe what I'll do is I'll ground it on three points in space, three points in time. Now, maybe a very near future, and then some point out in the distance and maybe some counter narratives, something else to think about with respect to jobs. In the near term, one of the most important things is that AI is not just AI as software, but it's not pre-recorded software, as you know. For example, Excel was written by several hundred engineers. They compiled it, it's pre-recorded, and then they distributed it as this for several years. In the case of AI, because it takes into the context what you asked of it, what's happening in the world, right? Contextual information, it generates every single token for the first time, every time. Which means every time you use the software and everything that we do, AI is being generated for the first time ever. Just like intelligence, our conversation today relies on some ground truth and some knowledge, but every single word is being generated for the first time here. The thing that's really quite unique about AI is that it needs these computers to generate these tokens every single time. I call them AI factories, because it's producing tokens that will be used all over the world. Now some people would say it's also part of infrastructure, the reason why it's infrastructure is because obviously it affects every single application, it's used in every single company, it's used in every single industry, it uses it every single country, therefore it's part infrastructure like energy and internet. Now because of that, and the amount of computers that's necessary to generate these tokens, and it's never happened before, and because we need these factories, three new industries have emerged. Number one, well three new type of plants have to be created. Number one, we have to build a lot more chip plants. TSMC is building SK Heinex, building a lot more plants, and so we need more chip plants. We need more computer plants. These computers are very different. These are supercomputers that the world's never seen before, right, Grace Blackwell looks like a very different type of computer than anything that's ever been made, and entire rack is one GPU, and so we need new supercomputer plants, and then we need new AI factories. These three plants are currently being built in the United States at a very large scale, quite broadly all over the United States for the very first time. The number of construction workers, plumbers, electricians, technicians, network engineers, right, the number of the late-scale labor that's necessary to support this new industry in the near term, it'll be enormous. Let's just face it, I'm so excited to hear that electricians are seeing their paychecks double. They're being paid to travels, like us, we go on business trips. They're going on business trips, and so it's really terrific to see that these three industries are now three types of plants, factories, are just creating so much jobs. The next part is the near term impact of AI on jobs, and one of my favorites is, I love Jeff Hinton, he said, some five, six, seven years ago, that in five years' time, AI will completely revolutionize radiology, that every single radiology application will be powered by AI, and that radiologists will no longer be needed, and that he would advise the first profession not to go into his radiology, and he's absolutely right, 100% of radiology applications are now AI powered. That's completely true, and in some eight years' time, it is now completely pervaded radiology. However, what's interesting is that the number of radiologists increased, and so now the question is why, and this is where the difference between task versus purpose of a job. A job has tasks and has purpose, and in the case of a radiologist, the task is to study scans, but the purpose is to diagnose disease, and that exactly under the research, and so in the case, in their case, the fact that they're able to study more scans more deeply, they're able to request more scans, do a better job diagnosing disease, the hospital's more productive, they can have more patients, which allows them to make more money, which allows them to want to hire more radiologists. The question is, what is the purpose of the job versus what is the task that you do in your job? As you know, I spend most of my day typing. That's my task, but my purpose is obviously not typing, and so the fact that somebody could use AI to automate a lot of my typing, and I really appreciate that, and it helps a lot. It hasn't really made me, if you will, less busy in a lot of ways. I'd become more busy because I'm able to do more work, so I think that's the second part to consider is the task versus the purpose of the job. This example really strikes home because my sister-in-law Aaron actually leads a nuclear medicine at Stanford, so she's in radiology, and with all the technology advancements that are coming, these doctors really welcome it, and they are working 20 hours a day trying to do more research and serve more patients. Exactly. And I think one thing that is often missed beyond the sort of diversity of jobs being created by this investment in infrastructure is actually how much latent demand there is for different goods that we need in society, like better health care. I don't think anybody feels like, you know what? We have reached the tip-top, mountain top of what American health care or global health care can be, and the more we can make these people productive, the more demand they will be. That's exactly right. In very old, it's more productive, it doesn't result in layoffs, it results in us doing more things. I met your new higher class today, you seem to be hiring every week anyway. That's exactly right, right? The more productive we are, the more ideas we can explore, the more growth as a result, the more profitable we become, which allows us to pursue more ideas, and so I think you're absolutely right that if the job, if your life, if the world, the problems, is literally already specified. And there's no other problem to solve than productivity would actually reduce the economy. But it's clearly going to increase the risk of economy. I think the next part that I would consider is, you know, people say, gosh, all of these robots that we're talking about, it's going to take away jobs, as we know very clearly, we don't have enough factory workers. Our economy is actually limited by the number of factory workers we have. Most people are having a very hard time retaining their workers. We also know that the number of truck drivers in the world is severely short. And the reason for that is people don't want those jobs where you have to travel across the country and live in different parts of the world, different parts of the country, you know, every single night. And so people want to stay in their town, stay with their families. So I think the first part is that having robotic systems is going to allow us to cover the labor shortage gap, which is really, really severe in getting worse because of aging population. This is not only in the United States, all over the world as you guys know. And so we're going to cover the labor shortage. But the second part that people forget, and as a result, there are shortages as well in other places that people talk about, AI being relevant, accounting would be an example where there's shortages there, nursing is another example. So you can go through multiple other industries and say, okay, there's gaps. That's right. And the AI is trying to help fill those gaps. And so automation is going to help us increase and solve the labor gap. Now people also don't remember that when we have cars, we need mechanics to take care of our cars. And if you look at the robot taxis that are even on the streets today, it's taken 10 years for that to happen. Look at all the maintenance crews and all of the various hubs that they're in where you have to take care of these robot taxis. Just imagine we have a billion robots. It's going to be the largest repair industry on the planet. So I think a lot of people don't, they just have to think through. And this is the part where you said, when we create this type of automation, we create this other job. Right now, look at AI is creating so many jobs. The AI industry is creating a boom of jobs. I think one of the core challenges here is it's very easy to draw a straight line of extrapolation from like, oh, you know, there are tools that help lawyers be more productive. It's going to replace the lawyers. But it's actually, it takes like a step of incremental reasoning to say, there's a sucking sound in the economy for everything in AI infrastructure. There's actually a sucking sound toward all of this demand that is latent in the places where we have gaps where I think a lot of policymakers have focused on, you know, we can't replace or reduce what we have when it's really there's there's far more demand in what we actually know. And in the case of lawyer, what's the, what's the purpose of the lawyer versus the task of the lawyer reading a contract, writing a contract is not the purpose of the lawyer. The purpose of the lawyer is to help you resolve conflict. And that's more than reading a contract. It's more than writing a contract. The purpose is to protect you. That's more than reading a contract, it's more than writing a contract. So I think just, it's really, really important to go back to what is the purpose of the job versus the task that we use, you know, to perform that job. That changes over time. Yeah. The other big thing that you mentioned that I think is really important to touch upon is both China is sort of in the rise of Chinese open source in particular where, you know, one of the highest scoring models against benchmarks in our Chinese models on the open source side down the closer side. It's still a lot of the US models, but things like Quinn, DeepSeek, et cetera, doing very well. You've long been a procurement for open source in general. Could you, could you share views about both China emerging for AI for open source and what the US should be doing in terms of open source as well as its own industries? When you think about these complicated interconnected, dependent networks of problems, these, you know, big goop of a mesh of problems, it's always good to go back and find a framework for what it is that we're talking about. In the case of AI, what is AI? Well, of course, the technology of AI and the capability, the capabilities of AI is about automation, is about automation of intelligence for the very first time. And you could combine it with megatronics technology to embody that megatronics and make it perform tasks. So that's what's AI automation, but what is the stack that makes AI possible? What's the technology stack, the functional stack, and of course, the easiest way to think about that is it's kind of like a five year, five year, five year cake, which is at the lowest level is energy, it transforms energy to the output that I just described. The next layer is chips, the next layer is infrastructure and that infrastructure is both hardware, software, right? This is where lamp power and shell, this is where construction is, data centers are the software stack, you know, for orchestrating the, so it's software and hardware. The layer above that is where everybody thinks about, which is AI, which is the models. We know this, but it's really helpful to understand that AI is a system of models and AI is a technology that understands information and there's human information. And so we oftentimes think about AI as a chatbot, but remember, there's biological information, there's chemical information, there's physical information, information of all kinds. There's financial information, there's healthcare information, there's information of all modalities, all kinds, AI is really, really broad. And of course, human language is at the foundation of many things, but it's not the essence of everything because as you know, you know, biology molecules don't understand English. They understand something else, right? Protease don't understand English, they understand something else. I think the next layer, the important thing is, is that's where the AI models are, but there's a hole that AI is very, very diverse. And then the layer above that is applications, and it depends on the industry and you already mentioned open evidence, you mentioned Harvey, there's cursor, there's all kinds of, right? There's all kinds of applications, full self driving is really an application, an AI application that is embodied into a mechanical car. And a figure is a AI application that has been embodied into a mechanical human. And so you got all these different applications. Well, this five layer stack is one way of thinking about it. And then the next way you're thinking about it, you just mentioned is AI is really diverse. When you now have this framework of what the technology capabilities are, how to build the technology and how diverse it is, then you can come back and think about, okay, let's ask the question, how important is open source? Well without open source, you know, today, of course, the frontier models, the leading labs have chosen to use a closed source application approach, which is just fine. You know, what people decide to do with their business models is really in the final analysis, they're business and they have to, they have to calculate what is the best way for them to get the return on investment so that they could scale up and make better advances. However, they made that calculus is fantastic. On the other hand, without open source, as you know, startups would be challenged, companies that are in different industries, whether it's manufacturing or transportation or it could be in healthcare, without open source today, all of that AI work would be suffocating. And so they just need to have something that's pre-trained. They need to have some fundamental technology about reasoning. From that, they could all adapt, fine tune, you know, train their AI models into exactly the domain and application they want. And so what people really, really miss is just the incredible pervasiveness and the importance of open source to all of these industries, large companies, without open source, some of a hundred-year-old companies that I work with in industrial spaces and healthcare spaces, they would be suffocated. They wouldn't be able to do that. Open source at this point is driving all of our data centers, it's driving a big chunk of telephony in the world in terms of Android or other devices, it's driving a lot of industrial applications. So it's already pervasive. And I think the big question is, open source without open source, higher ed. Higher ed wouldn't happen. And education research startups, I mean, the list goes on, you know. So we talk all day long about the tip, the most visible part of that, the part that's most newsworthy, maybe, but underneath that is such an important space of open source AI. And whatever we decide to do with policies, do not damage that innovation flywheel. So I spend a lot of time educating, educating policy makers to help them understand whatever you decide, whatever you do, don't forget open source, whatever you decide, whatever you do, don't forget biology. I think the counter narrative here that is worth addressing is that essentially like, you know, there should be a monolithic vertical player and monolithic asset in the like one model that does it all. And then we can't give way that from jewel to other countries or non-American companies. And your argument is like, we actually need this huge diversity of applications. And the American advantage is actually, or any sovereign advantage is in the whole stack, right? The capability to deliver any piece of it. I guess someday we will have got AI, but someday, but that someday, that someday is probably on biblical scales, you know, I think galactic scales. I think it's, it's not helpful to go from where we are today to got AI. And I don't think any company practically believes there anywhere near God, and nor do I do I see any researchers having any reasonable ability to create God, the ability to understand human language and genome language and molecular language and protein language and amino asset language and physics language all supremely well. And God AI just doesn't exist. And yet we have a lot of industries that need AI. AI is, if you will, at the simplistic level, it's just the next computer industry. And give me an example of a company and industry, a nation who doesn't need computers. And we all don't have to wait around for God AI for us to advance, right? So God AI is not showing up next week. I'm fairly certain of that. And God AI is not not going to show up next year, but the whole world needs to move forward next week, next year, next decade. I think that that the idea of a monolithic gigantic company, country, nation state that has God AI is just, it's unhelpful, it's too extreme. Then in fact, if you want to take it to that level, then we ought to just all stop everything. It's the point of having even governments. I mean, why are they doing policies? God AI is going to be smart enough to avert, you know, work around any policy. And so what's the point? And so I think that we ought to bring things back to the ground, ground level and start thinking about things practically and use common sense. This seems to be a big theme in general in terms of this conversation where there's been a lot that's been kind of put out there that seems very extreme if you actually think about it. That's an employment. Nobody is going to be able to work again. It's God AI is going to solve a big problem. We shouldn't have open source for X-wise, you reason, despite open source powering much of our industries already. And so it seems like in general, maybe one of the themes of 2025 was there's a lot of extremes that were sort of painted in the public with AI that if you look at them very closely, don't really follow a logical chain in terms of happening any time soon. And so it sounds like it's really important to have this conversation. It's extremely hurtful, frankly. And I think we've done a lot of damage with very well-respected people who have painted a doomer narrative end of the world narrative, science fiction narrative. And I appreciate that many of us grew up and enjoyed science fiction. But it's not helpful. It's not helpful to people. It's not helpful to the industry. It's not helpful to society. It's not helpful to the governments. There are a lot of many people in the government who obviously aren't as familiar with, as comfortable with, the technology. And when PhDs of this and CEOs of that go to governments and explain and describe these end of the world scenarios and extremely, extremely dystopian future, the future, you have to ask yourself, you know, what is the purpose of that narrative? And what are their intentions? And what do they hope? Why are they talking to governments about these things to create regulations to suffocate startups? For what reason would they be doing that? You know, and so. And do you think that's just regulatory capture where they're trying to prevent new startups from showing up and being able to compete effectively or what do you think is the goal of some of these conversations? You know, I can't guess what they have in mind. I know that the concern is regulatory capture. As a policy, as a practice, I don't think companies ought to go to governments to advocate for the regulation on other companies and other industries, just in practice. Their intentions are clearly deeply conflicted and their intentions are clearly, you know, not completely in the best interest of society. I mean, they're obviously CEOs, they're obviously companies and obviously they're advocating for themselves. And so, I think if we can all come back to where are we today and think about where the technology is going to be, I mean, literally in one year's time, as we were talking about in the beginning, some of the most proud moments is when the industry was able to invest very aggressively in advancing AI technology instead of being slowed down. Remember, just two years ago, people were talking about slowing the industry down. But as we advanced quickly, what did we solve? We solved grounding, we solved reasoning, we solved research. All of that technology was applied for good, improving the functionality of the AI, not, you know. Yet the end has not come. It's become more useful, it's become more functional. It's become able to do what we ask it to do, you know, and so the first part of the safety of a product is that it performed as advertised. The first part of safety is performance that it's supposed, like the first part of safety of a car isn't that some person is going to jump into the car and use it as a missile. The first part of the car is it works as advertised 99.99% of the time working as advertised. And so it takes a lot of technology to make that car or make that AI work as advertised. And I'm really glad that in the last couple of two or three years, the industry has invested so much in enhancing the functionality of the AI as advertised. And I think if we're to look at the next 10 years, we have so much work to do to make it work as advertised. Meanwhile, as you both of you invest so much in the ecosystem, you see so many companies being built for synthetic data generation so that the AI's could be more grounded, more diverse, less biased, more safe. You're investing in a whole bunch of companies in cybersecurity using AI for cybersecurity, right? People think that there's this AI. The marginal cost of the AI is going to go down significantly and it is. And therefore, the AI's going to be dangerous. It's exactly the opposite. The marginal cost of AI is going to go down significantly. That one AI is going to be monitored by millions of AI's. And more and more AI is going to be monitoring monitoring each other. People can't forget that an AI is not going to be an agent by itself. It's likely the AI is going to be surrounded by agents monitoring it. And so it's no different than if the marginal cost of keeping society safe was lower, we have police in every corner. So one thing that we were talking about a little bit earlier was just the cost of AI and how it's been coming down. And so I think in 2024, the cost of GPT-4 climate models, if you look at a million tokens, it came down over 100x. You know, so many of my team did this analysis to show that. So the cost of dropping pretty dramatically and very rapidly in part of it is all the advancements. You all have been driving on the video level, but also it's across the stack. We'll be getting big efficiency gains. At the same time, model companies are talking about how the cost of rising, how there's enormous sort of capital modes to building these things out. How do you think about cost of training and cost of inference over time and what that means for the average end user or the average start of company trying to compete or people trying to do more in this industry? I forgive this statistic, but you know, Andre, Andre Carpathi estimated the cost of building the first batch of GPT, I think versus now, I think you could do that on the PC now. Yeah, it's probably tens of thousands of dollars at that point or maybe even less. Right. And so it costs nothing. It's an open source project that you can do in a weekend. Oh, is that right? Okay. That's incredible, right? Yeah. We're talking about three years. What people said cost billions of dollars, it's a super computer's built, raising billions of dollars in order to do all that now, cost, you know, something that you can do on a weekend on a PC. So that tells you something about how quickly we're making, making AI more cost effective. Where's Spark? Sorry, probably not quite a PC. Yeah. Okay. Not quite a PC. Yeah. We're improving our architecture and performance every single year. The first GPT, I think was trained on Volta's. And then Ampere, you know, and it wasn't, I think the first breakthroughs, none of it included a hopper. And of course, hopper last couple two, three years and we're often black well for last year and a half or so. And every single one of these generations, the architecture improves and of course, the number of transistors go up and the capacity goes up. Every single generation very easily every, every single year from a computing perspective, the combination of all that, getting five to 10 X every single year is not unusual. And here comes Ruben just around the corner. And so we're seeing five to 10 X every single year. Well, compounded, it's incredible. Moore's Law was two times every year and a half. And over the course of five years is 10 X, over the course of 10 years is 100 X. In the case of AI, over the course of 10 years is probably 100,000 to a million X, okay. And that's just a hardware. Then the next layer is the algorithm layer and the model layer, the combination of all that, the fact that if you were to tell me that in the cost in the in the span of, you know, 10 years, we're going to reduce the cost of token generation by a billion times I would not be surprised, okay? And so that's the tokenomics of AI. On the training side, it's not quite as aggressive in cost reduction, but it's close. If you were to say that every single year we're increasing by two or three X over the course of 10 years, incredible. The important idea is when somebody says it costs a hundred million dollars to train something or a half a billion dollars to train something, well, next year it's 10 times less, next year is 10 times less. But people just scale these things up though, right? So the kind of argument is, well, we'll just get bigger every year by 10 X or 100 X or, you know, we'll try and offset that decreasing cost by scale. Mm-hmm. And others may keep up. Yeah. But really what's happening is you're, and this is where MOUs come in, as you know, the scale went up by a factor of 10, but the computational burden did not go up by a factor of 10, because you're getting the compounded benefits of all three things. The hardware's going up, the algorithms of the training models are going up, and of course the model architecture is going up, and we're getting the benefit of learning from each other. This is, you know, let's face it, deep seek was probably the single most important paper that most Silicon Valley researchers read from in the last couple of years. It was the only thing that felt frontier. That was open. That's right. In years. That's right. Because it came a lot of a lot of your open source again. Yeah. Yeah. Literally deep seek benefited American startups and American AI labs all over. And infrastructure companies. And infrastructure company all over. Probably the single greatest contribution to American AI last year. And so if you said this out loud, of course, you know, people kind of shudder that were American AI is actually getting learning from and benefiting from AI from other nation. But why would that be surprising? You know, AI researchers in all over America, all over America, Chinese natives and come from different countries. We benefit from every country, we become benefit from every researcher and know all of the world's ideas don't have to come from United States. And so I think back to your original question. It is the case that some of the narratives are around the cost of AI is about scaring everybody out of the market, you know, nobody ought to do pre-training but us. Nobody should do training these frontier models but us. But because of innovation of models, algorithms and the computing stack, the cost of AI is actually decreasing well more than 10X every single year. And so you're just one year behind or even six months behind, you could really stay close. And I think one thing that felt very different to me about 2025 is Ilya said recently that, you know, we're in the age of research again versus an age of scaling. I think both things are happening, by the way, everybody is also trying to scale on multiple dimensions. Yeah, exactly. Both are happening. You know, being six months behind or being at 100 versus a 200K cluster, I think matters if you are competing symmetrically. But now you have people from frontier labs or at the very top of the game, who have very different ideas about how to cross from here or who are working on diversity problems. That's right. And I think that felt different from 24, maybe where there was a lot of energy focused on just pre-training scale in LMS. Yeah. Several other dynamics. Because the market grows, each one of these models could choose to have verticals or segments where they want to differentiate. Somebody could decide to be a better coder. Somebody could decide to be just better at being easier to be accessible so that it could be a greater consumer product. You know, the diversity of these models, as a result, you could, you could probably make a niche leap without having to be graded everything else and still be super valuable to the market. It's no longer necessary to boil the entire ocean. The for two years ago, because it was called pre-training, people said, well, you know, pre-training is over. First of all, pre-training is not over. But the point of pre-training is to train yourself for training. That's why it's called pre-training. To prepare yourself to do the real training. And now we call it post-training. It's kind of weird. I think it's just training. But pre-training is -- pre-training, and therefore it's training. Training, as we all know, is where a compute scaling directly translates to intelligence. You've largely -- now the data necessary to train the model is actually pretty small. Maybe it's just a verifiable results. Now, it's really algorithmic, very compute-intensive, and so -- and you don't have to be good at everything in life, as you know. It's like all of us. We don't -- we could decide, because we don't have time to learn everything equally well. We decided to choose a specialty and focus all of our energy on it, and we become superhuman or incredibly good at something that other people are not. And so, I think AI ladders are going to start doing the same. They're going to start bifurcating into various segments, and over time, you're going to -- and startups will do the same. They'll find a micro niche, and they'll take something open, and then be incredibly good at it. Most optimistic views here is actually that these micro niche are quite valuable, right? I was talking to Andre because I'm talking a lot of people about the predictions for next year. We'll ask you yours as well, of course. But he asked, you know, what's an example of a prediction that would have been prescient last year? And my answer, everything's easy and retrospect, is that coding would be the first application level business, like it's to a billion of ARR as an AI native app, right? I think if you'd taken an old world view of this, you would have believed we're like one of two narratives, right? One is single model does everything, and it'll all just be some zoomed into something modelistic. And two is that developer tools never get very big, right? Well, it kind of depends on how valuable the developer tool is. Now, I think many more people understand software engineering is an niche, and there's more demand than ever for it. But I think we'll see more like that next year. So interesting, we are using, we use cursor here and we use cursor pervasive here, every engineer uses it and a number of engineers, you just mentioned it. The number of people we're hiring today is just incredible, right? Monday has come to work on a video day, and why is that? This is now the purpose and the task. The purpose of a software engineer is to solve known problems and to find new problems to solve. Coding is one of the tasks. And so if the purpose is not coding, if your purpose literally is coding, somebody tells you what to do, you code it. All right, maybe you're going to get replaced by the AI, but most of our software engineers, all of our software, they're goal is to solve problems. And it turns out we have so many problems in the company, and we have so many undiscovered problems. And so the more time they have to go explore undiscovered problems, the better off we are as a company. When we give me more joy, then if none of them are coding at all, there's just solving problems. You see what I'm saying? And so I think the framework of purpose versus task is really good for everybody to apply. For example, somebody who's a waiter, their job is to not to take the order. That's not their job. It turns out their job is so that we have a great experience. And if somebody, if some AI is taking the order, their job or even delivering the food, their job is still helping us have a great experience. They would reshape their jobs accordingly. So I think the question about cost of compute is really important. Let's, let me come back to one. The reason why we are so dedicated to a programmable architecture versus the fixed architect, remember a long time ago, a CNN ship came along and they said, Embediat done. And then a transformer ship came and Embediat was done. People are still trying that, yes. Yeah. And the benefit of these dedicated ASICs, of course, it could perform a job really, really well. And Transformers is a much more universal AI network. But the Transformers, you know, the species of it is growing incredibly. The attention mechanism. The attention mechanism, how things about context, diffusion versus auto regressive, the hybrid SSM transformers, hybrid SSMs. For example, Neemotron, we just announced a new hybrid SSM. And so the architecture of Transformers is in fact changing very rapidly. And over the next several years, it's likely to change tremendously. And so we dedicate ourselves to an architecture that's flexible for this reason so that we can, on the one hand, adapt with, remember, because Moore's law is largely over transistor benefit. It's only 10%, 10% maybe a couple of years. And yet we would like to have hundreds of X every year. And so the benefit is actually all in algorithms. And an architecture that enables any algorithm is likely going to be the best wine, right? Because the transistor didn't advance that much. And so I think the, our dedication to programmability is number one for that reason. We have so much optimism for innovation and algorithms and innovation software that we protect our programmability for that reason. The second thing is, is by protecting this architecture, our install base is really large. When a software engineer wants to optimize their algorithm, they want to make sure that it doesn't run on just one, this one little cloud or this one little stack. They wanted to run on as many, as many computers as possible. So the fact that we protect our architecture compatibility, then flash attention runs everywhere. So SSM's run everywhere, diffusion runs everywhere, odd regression runs everywhere. It's just, depending, it doesn't matter what you want to do CNN still run everywhere, LSTM still runs everywhere. And so that this architecture that is architecture compatible so that we have a large install base, programmable for the future is really important in the way that we help to advance. And as a result, all of this drives the cost down. And I'm super proud that, that our latest innovation, MV link 72, we're the lowest cost token generation machine in the world by enormous amounts. And the reason for that is because MOE's are really, really hard. And so, you know, people didn't expect that. That for MOE's, it's probably easier to train, but for inference, it's incredibly hard to generate tokens on. As, as cost drop, usually you open up new applications or new verticals that become more and more accessible. And we talked a little bit about coding, like cursor and cognition and other companies that are really benefiting from that in the last year. Do you have any thoughts or predictions in terms of what the next breakthrough industries will be or new applications or areas that you're most excited about coming in 26 in particular, like other one or two things that you think, because of three things, I, because of, because of a couple of two, three things, I think, I think several industries are going to experience their chat GPT moment. I believe that multi-modality and very long context is going to enable, of course, really, really cool chatbots. But the basic architecture, that in combination with breakthroughs and synthetic data generation is going to help create the chat GPT moment for digital biology. That moment is coming. And by digital biology, do you specifically mean other aspects of like protein folding and protein binding or protein diagnosis? I see. Protein synthesis. I think we're good at protein understanding. Mm-hmm. Now, multi-protein understanding is coming online, and we recently created a model called Lot Pratina. It's opened. It's for multi-protein understanding and representation learning and generation. So I think that the protein understanding is advancing very quickly. Now protein generation is going to advance very quickly, chat GPT moment, proteins. Yeah, there are a lot of interesting companies working on molecule design in N10 way, like I. Exactly. Exactly. And then, of course, chemical understanding and chemical generation. And then protein, chemical confirmation, understanding and generation, is that right? And so that combination, the chat GPT moment, the generative AI moment, all of that stuff is coming together for digital biology. And to your point about like new industries or, you know, the way I think about it is like investing in the inputs for this AI as well, all of these things around biology and chemistry and material science. They require real world data generation and experimentation, and that's a new infrastructure too. New infrastructure, synthetic data is going to be really important because they just have such sparse sparsity of data and they just don't have as much as human language. And there, the real breakthrough is going to be when we can train a world foundation model, a foundation model for proteins, a foundation model for cells. I'm very excited about both of those things. Once we have a foundation model, our understanding capability, our generative capability, that data fly wheels, we're going to take off. The second area that I'm excited about, of course, reasoning made huge breakthroughs in language. But because of reasoning, cars are going to be able to perform better. So instead of just perception cars and planning cars, there are going to be reasoning cars. So these cars are going to be thinking all the time and when they come up to a circumstance they've never encountered before, they can break it down into circumstances they have encountered before and construct a reasoning system for how to navigate through it. And so the out of domain, out of distribution, part of AI is going to very much be addressed by reasoning systems. And as a result, we could do more things than we were taught to do between generative AI and multimodal vision, language, action models and reasoning systems. I think we're going to see big breakthroughs in human and robots or multi-embodiment robots. What do you think is a timeframe for that? Because if you look at the self-driving analog and obviously self-driving technologies were based on very different types of neural networks and what we're using today. There's been a big swap over the last two, three years in terms of how we do a lot there. You started too soon. Self-driving cars really had four errors. The first error was smart sensors connected into a car, the mobile error. The mobile error. And even even the very earliest days, the waybook, yeah, even the earliest days of Waymo, you're using smart sensors, a lot of human engineered algorithms, and although it's case in beer mapping, extreme happening, mapping and then different systems for planning and perception. Exactly. And so you're essentially creating a car that is driving on digital rails, right? So no different than the rails at Disneyland, except there are digital rails. And so that's the first generation, the second generation. And during that generation, you have perception, world model, and planning, and these modules. And each one of these modules have the limits of their technology, and perception was first affected by deep learning first, and then it propagated through the pipeline. And so that system was too brittle, and it only knows how to perform what you caught it. And now where we are, our end to end models, and then, and then where we're going to go next, our end to end models. We're freezing. Yeah, there you go. So that those are kind of the four errors. In a lot of ways, if we were to start a self-driving cars, probably three years ago, we get it. Yeah, it would probably be exactly the same place. All our poor friends who were working in self-driving, yeah. And I don't mind it. I've been working on it for 10 years. In various self-driving car stack, by the way, number one rated safety in the world today. Number one, we just got, we just got that rating today last week, and number two is Tesla. So I'm very proud of the two working companies right up on this. Are you? So from a robotics perspective, you think, because we've already built all these works of technologies in the modern era, robotics won't have the same 10, 15-year-old dog. That's right. That's right. I'm much more optimistic with robotics because we've been through some days to get technology. Now, you know, people are thinking about human robotics. Human robotics has a lot of challenges. I mean, there's all the mega-tronics challenges. Yeah. Like, for example, it's not helpful if the robot weighs three-hundred pounds, and what happens if it falls over and is interacting with kids and so on and so forth. And so, so you got all kinds of challenges to deal with. I'm certain that we're going to solve those. But remember, the fundamental technology that goes into human robot, robot, can go into a pick and place robot. It could be, it could be how do you think about one thing I've been curious about for robotics in particular is if I look at who won or who this perceived is winning in self-driving, it's largely incumbents, right? It's Waymo. It's Tesla. You mentioned the safety rating and video's gotten, and so it's people who've been working on this for a long time. It took a lot of capital. It was really intensive to get there. You have supply chain. You have hardware. You have all this extra complexity. I think the same thing will be true in robotics. So the winner is basically going to be Tesla with Optimus and other people who have both been in the industry for a while, but also have all those sort of incumbent effects. Do you think it was room for start-ups? They will be one of the leaders, one of them, and it's surely a major one. But everything that moves will be robotic. Everything that moves will be robotic. And everything that moves is a very large space. It's not all human or robot. Yet every AI will be multi-embodiment, meaning just like a human with our multi-embodiment AI ourselves, we could sit in a cart and embody that. We could pick up a tennis rack and embody that. We could pick up a chopstick and body that. And so we could embody the people our dental purpose, right? That's true. Exactly. And so AI is going to become general purpose. So you have one arm pick and place. Maybe it's two arms pick and place, could be six arms pick and place. So I think you're going to have all kinds of different sizes and shapes. It could be a caterpillar. It could be an excavator, it could be all kinds of stuff. And so AI will embody those, just as a construction worker embodies an excavator, embodies attractor. Could there be a small number of companies then that do the embodiment for everything? Are you saying more? I can definitely see a lot of software companies. And then those, that software company could serve a lot of different verticals. But each one of the verticals will still have solution providers that then grounds it all turns it into something that works perfectly. Does it make sense? Because in the case of AI for consumers, if it works 90% of the time, you're delighted. Mind blown. If it works 80% of the time, you're satisfied. In the case of most industrial and physical AIs, if it works 90% of the time, nobody cares about that. And the 10% that it fails, 100% dissatisfaction. And so you've got to take it to 99.9999. So the core technology might be able to get you to 99% and then that's a vertical solution provider, like a caterpillar or somebody. They could take that core technology and make a 99.9999% grade. Do you think that's what happens like early a storm? Because in markets that are this immature, it seems one of the fastest past market could be full verticalization, because you just have control of iterations. The difficulty of verticalization for technology that is general purpose is that you don't have the R&D scale to build a general purpose technology. Now, of course, open source helps that tremendously, which is the reason why you're going to see a big surge of vertical opportunities in AI in the next several years. My prediction would be over the course of the next five years. The excitement is going to be verticalization. Notice, we're excited about open evidence, we're excited about Harvey, we're excited about cursor. Cursor is a horizontal, but it's kind of a horizontal vertical, you know, and so I'm super excited about all the verticals. You know, a lot of people said, yeah, AI is going to get so got AI is going to get so good that all these wrapper companies are going to be obsolete. It just misses the big point. The reason why you could talk about, the reason why somebody can talk about, somebody is creating technology, you could talk about the life of a surgeon is because they've never been a surgeon. The reason why somebody who builds at AI and talks about the life of a accountant and attacks, you know, attacks experts because they've never been a tax expert, you know, and so I think they just, you know, the reason why somebody could talk about being a bus boy without being a bus boy is they remember being a bus boy. So I think you've got to be a little bit more empathetic about the depth of the complexity of the work and try to truly understand the purpose of the work. Oftentimes, the technology addresses the task, it doesn't address the purpose. So I guess one of the other narratives from, we're looking at narratives that are true versus not true, you know, for 25, one other narrative that's come up has been more about energy and energy utilization and what we have enough energy to support AI, how do you think about that? On the first week of President Trump's administration, he said, drill, baby, drill, he did so much flag for that. If not for this entire change in sentiment about energy growth in our country, we can all concede now. We would have handed this industrial revolution to somebody else. And we're still power constrained. We're still power constrained. Without energy, there can be no new industry. And of course, we've been energy starved now for what a decade. If not for the fact that President Trump reversed that narrative, we would be completely screwed. Without energy, you can't have industrial growth. Without industrial growth, the nation can't be more prosperous. Without being more prosperous, we can't take care of domestic issues. We can't take care of social issues, you know, on and on and on. And so the fact that matters, we need energy to grow. We need every form of energy. We need, you know, natural gas. We need to be, of course, we need more energy on the grid. We need more energy behind the meter, we're going to need new clear. When is not going to be enough, solar is not going to be enough. Let's just all acknowledge that we'll take it, we'll take everything we can. But the fact that matters, I think for the next decade, natural gas, you know, is probably the only way to go forward. What's really interesting is I agree the timeline is too far out to address people's, you know, power generation issues in 27 and 28, where, you know, large players, building clusters are very concerned. But the biggest drivers of like climate innovation in the US have actually been as a result of this AI infrastructure problem, right? Because people look at the demand. Finally, that's right. They look at the demand and the demand is driving people to create massive new battery company, solar concentrators, it's put new energy, new energy, like, you know, it's so interesting behind SMRs. The AI industry is driving all of that sustainable energy industry, because people see that there is going to be demand. That's right. Right. So even if, and I think there is no practical answer in the small number of years time frame versus large gas, right? It still drives climate innovation. Yeah. No question about it. And I think that's exactly right that, that, you know, doomer messages causes policy and that policy may, may affect the industry in some way, but there's nothing more powerful than demand. Look at all the jobs it's been created. Look at all the industries that's been formed around it, sustainable energy, likely. And history, realized it, as Sarah, I think you're going to be absolutely right, that, that if not for AI, well, AI was probably the biggest driver for sustainable energy ever. Yeah. A friend of mine has a saying that, uh, doomers of the people who sound smart at dinner parties and optimize the people who drop humanity forward. And I think that's very true for all these things we've talked about. Yeah. Yeah. It's really true. Yeah. Well, that's one of the big, big takeaways for, for this last year, the battle of narratives. Right. And it's too simplistic to say that everything that the doomers are saying are irrelevant. That's not true. A lot of very sensible things are being said. It is too simplistic to say that when somebody is optimistic, that they're just naive. It needs to be grounded in reality. Yeah. That optimistic people are just naive, you know, and that's obviously not true. But I think we just have to be mindful of the balance of it. 90% of the messaging is all around the end of the world and do, and the pessimism. And, you know, I, I think we were scaring people from making the investments in the eye that makes it safer, more functional, more productive, and more useful to society. And so we just, you know, more secure, you know, all of that takes technology, security text technology, safety text technology. I appreciate that my car is safer today because it has better technology than a car 50 years ago. And so, so I think it takes technology to be safe, technology to be secure. And so I'm delighted to see that the advancement of technology is still accelerating and ongoing. And so we just have to make sure that the policy makers around the world, the governments are able to are thinking about balancing these two ideas. How do you, so I guess we've talked a lot about 25 and the narrative is 25. How do you think about 26? What are you excited about? What do you see coming? I think our big change is that we should be aware of. I am optimistic that that our relationship with China will improve, that President Trump and the administration has a really, really grounded and common sense attitude about and philosophy around, around how to think about China, that they're an adversary, but they're also a partner in many ways. And that the idea of decoupling is naive and the idea of decoupling for whatever reason, philosophical reasons or national security reasons, it's just not, it's not based on any common sense. And the more you, the more deeply you look into it, the more the two countries are actually highly coupled. Both countries ought to invest in their own independence. When you depend too much on someone, their relationship becomes too emotional, as you know. And so it's good to have some independence or as much independence as either would like, but to recognize that there's a lot of coupling, a lot of dependence between the two countries. And I think there needs to be a nuanced strategy, a nuanced attitude about how to manage this relationship in a productive way for all of the people of two countries and for all of the people around the world. Everybody depends on a productive, constructive relationship of the two most important nations and the single most important relationship for the next century. And so we have to find that answer. And I'm just really delighted that President Trump is looking for a constructive answer. And so I think the next year will be a much better, better, better year than the last several. I'm happy with the administration was able to suggest a, an expert control policy that is grounded on national security, recognizing that they already make so many chips themselves. And they, they can depend on Huawei themselves for their military, for their national security. They got ample technology to do that. And so that American technology, although general purpose, it is unlikely to be used by their military because their military is too smart, just as our military is too smart to use their technology. And so it's grounded on national security. It's grounded on technology leadership. It's grounded on national prosperity. You know, one of the things that we just always have to remember is that the world's mightiest military is supported by the world's mightiest, economy. And so the, the wealth that we generate brings jobs home, creates prosperity in the United States, provides for tax revenues and ultimately funds the mightiest military on the planet. And so that circular system, that interconnected system requires a nuanced strategy. And, and I'm pleased to, to, to see some of the progress in that area that allows American technology companies to keep America first and keep America ahead and to, to support American technology leadership on the one hand, to win globally. And, and then, and then China, of course, is sorting itself out. You know, I mean, and also working, but they're sorting out the attitude about how to think about American technology. And there, because the historical argument there has been that if you look, for example, the internet, there was what was known as a great firewall, right? China basically prevented U.S. competition into China while the opposite was an issue. You know, there's been mass expatriation of U.S. jobs in industry to China as sort of part of the development of the 90s and 2000s. And so I think a lot of the things that people have brought up from a China U.S. policy perspective, besides just the military, adversarial relationship, where spheres have been flowends or, you know, all the various things like that is also just that economic imbalances that have been perceived to exist between the two countries. The way that I would think through that is go back to the first principles of technologies again. And let's say the internet, you have the chip industry, you have the systems industry, the software industry, you have the services industry on top. Remember, China's internet growth has been a boom for Intel and AMD selling CPUs, across selling DRAMs, SK, Hynex, and Samsung selling DRAMs. It is the second largest internet market for American technology industry. And so, so maybe, maybe it wasn't helpful to some layer of the stack, the Google's of the world. That's right. But don't exclude every layer of the stack. Always come back. Every single one of these things, take a step back and look at the whole stack. Maybe that's a theme for today as well and it makes sense that you would, you would send this message, but technology acts actually not just the sort of internet software application layer that's been very dominant for a few decades. That's right. It's the whole stack. And remember, as Intel and AMD prospered with the internet industry in China growth, the China industry growth, don't forget China also contributed tremendously to open source. No country in the world contributes more to open source than China. And look at all the startups here in America that were able to benefit from out of that open source to create the new startups that are here. And so you can't look at one area in isolation. You have to look at the whole life cycle with the technology and look at every layer of the stack. Doesn't make sense? When you take a look at that from that lens, China's internet industry generated enormous prosperity for America. Just not at the internet company per se. And Genshin, my other investor friends will not forgive me if I don't ask you about 2026 on the business side. Are we in an AI bubble? AI bubble. Yeah. There's a lot of ways to reason through that. And so again, when asked that question, my mind goes to what is AI and where are we in that? There's AI, then there's computing. As you know, Nvidia invented accelerated computing. Accelerated computing does computer graphics and rendering, AI doesn't. Accelerated computing does data processing, SQL data processing, AI doesn't. Accelerated computing does molecular dynamics and quantum chemistry, AI doesn't. These are all things that people could say someday AI will, but it doesn't today. Accelerated computing is really essential for classical machine learning, XG boost, computer systems, the whole process of feature engineering, extract, load, and transform. That entire data science machine learning's life cycle. Accelerated computing is used for all of that. The first thing to go to is in the context of Nvidia, what we see is the dynamic is a shift from general purpose computing to accelerated computing because most of us largely end it. You can't use CPUs for everything anymore, like you used to. So it's just no longer productive enough. It's not deflationary enough. So we have to move towards a new computing model and that's where Excel ready comes in. If genitive AI, well excuse me, if chatbots, let's just go open AI and that's raw bacon, Gemini. If none of that existed today, Nvidia would be a multi-hundred billion dollar company. And the reason for that is because, as you know, the foundation of computing is shifting to Excel ready computing. That's the first thing to realize is to take us that back and ask yourself, what is actually happening? Nailed the next layer of the question about AI now becomes, what is AI? Now we ask that, we ask the AI bubble question and we always go back to open AI's revenues. A hundred percent, don't we? You ask somebody, hey, is there an AI bubble? Everybody goes directly to open AI's revenues. First of all, if open AI currently has twice the capacity, their revenues will double. Because they know that. If they have ten times the capacity, they're, I really believe their revenues will ten times. And so they need capacity. This is no different than Nvidia needs way first from TSMC. Just because Nvidia exists and we're doing great, doesn't mean we don't need capacity, we need capacity, we need capacity of demand. And so in our world, it's sensible to everybody, we need capacity. Well in their world, they need factories. And if they don't have factory capacity, how do they generate tokens? Which is where we started our conversation today. And so they need factory capacity in order to increase their revenue growth. But nonetheless, we also said that AI is more than chatbots. It includes all these different fields of science. And Nvidia's AV business is coming up on $10 billion. Nobody ever talks about that. And you have the train world models. You have to train these AVs and happening robotaxies happening all over the world. Our AI work with digital biology, our AI work, and financial services. The whole industry of quants, quantitative trading is moving to all of that. Yeah, exactly. They used to be classical machine learning, a whole bunch of human features. They call quants, these specialized mathematicians, we're trying to figure out what the predictive features are. Now we use AI to figure it out. And so in order to have, instead of having quants, you need a lot of supercomputers. Digital services one are fastest growing segments, billions of dollars in quants, you know, in financial services, billions of dollars in AV, billions of dollars in robotics coming up, billions of dollars in digital biology. And so how big can that all that be? Well, simple logic is this, simple math. Whether you think that AI is going to replace, shortage, labor shortage or workforce shortage in any kind, let's ignore that for a second. The world is at $100 trillion in GDP, out of that, let's just say 2%, 2% annually is R&D. And let's just go back and tell you, five years ago, if you were to take the largest drug discovery company in the world, drug company in the world, and where's all of their R&D wet labs? Today, what are they doing, building supercomputers? And so there's a fundamental shift in how they think about that $2 trillion. It used to be $2 trillion for the old way of doing things. It's not going to be $2 trillion in the AI way of doing things. While $2 trillion is going to need $2 trillion of R&D is going to be powered by a whole bunch of infrastructure. And that's the reason why we're building supercomputers everywhere around the world. And so I think if you reason about it from the outside in, you know, either from the foundation up from the outside in, you come to the conclusion that what we're experiencing, what all three of us are experiencing, which is the amount of computing demand is insane. Give me an example of a startup company that goes, no, we're good. They are all dying for computing capacity. Give me an example for a researcher in any university, a scientist in any company who says, got plenty of capacity, everybody is dying for capacity. And so we have a global multi company, multi industry shortage. It's not just about open AI, even though open AI could use a lot more capacity as well. So I think, I think how we think about this, what, what the narrative, the narrative is not helpful. And it's a little bit too superficial to say, how do you prove there's an AI bubble? $12 billion of revenues, hundreds of billions of infrastructure being built. It's a little bit too simplistic. Yeah. The other thing pitch. Well, I'm 10 to point out is the MIT study. There's some study that I think came out of MIT that claimed that most enterprise deployments of AI weren't that useful, and you're like, well, did you do the change management? Did you do a re-work? Did you integrate into tooling? Did you, like, how long did it even take to implement it? If a planning cycle in an enterprise is a year, it uses something in six months. And so it feels like there's a lot of these kind of, again, overstated things that get a lot of attention. But then you map it against what's actually happening. Yeah. And the growth of these companies using AI and it's just a completely different world. That's right. And if you want to find out where the world's innovations happening, I would not go find out at an enterprise. Would you guys agree? Yeah. Enterprise is like the slowest adopters of new technologies. I would go talk to all of the startups, the 30, 40,000 startups that are currently doing this stuff. I would go talk to OpenEvidence. How's it working? I would go talk to Kursher. How's coding working, by the way? Yeah. I would just go talk to these people. I think it's really interesting that you see that, of course, you do have companies making, you know, 100 million dollar plus, multi-hundred million dollar plus progress of ARR in enterprise sales, Harvey Sierra, et cetera. But some of the fastest growing companies have been end user adopted, even in conservative industries, right? Yeah. Like healthcare or, you know, skeptical industries like engineering. Health care. The most, right? The most conservative of all. Yeah. But guess what? They are so concerned about getting the right answer, that the ability to have something like OpenEvidence, to do grounded research, high quality research, and get that, get that research as information to you. Nobody wants to do research. They want answers. Right? A great, just a great example of that. Yeah. Where they're basically making it really easy to do the physician notes, instead of the position sitting there and doing it. Back to your point on task forces. Task forces pretty exact. And I think a different way to think about the demand is like, there are so many jobs where you're asking, the work is actually like an impossible ask, right, of a doctor or a radiologist, keep up with the world's biomedical knowledge. Yeah. And R&D, which is accelerating, you know, computing and otherwise. Just like archive papers. Yeah. There was a time. Yeah. I've been trying to read it. Read it. Yeah. Both used to do. I'm going to do that anymore. I still try. But now I just load it all into chat, you know. Now I just load it all in with all of the ones that are interesting, and then I make it learn it. Yeah. And then I, you know, make it some rise. Like another summary. I interact with it, but the point is, we used to do search. We don't do it anymore. I don't do search. We used to do research, you know, the goal is to get answers, the goal is to get smarter. And these AIs allow us to help us do all that. And I think all of it, all of it comes back with, it's all more helpful if you come back to the framework that says AI is a multi-layer cake, and that AI is not just a chatbot. AI is very, very diverse in all of the industries and modalities and information and applications that it addresses. When you think about wanting to win, that America should win AI. It should not just be America should have this company win AI, but we should try to win across the board. And across domains. Across domains, exactly. And when we think about open source, all of a sudden, this is a helpful framework. When we think about winning, it's a helpful framework. When we think about energy is a helpful framework that because we need factories, factories need energy. And without energy, we have no factory, without factories, we have no AI. That's a helpful framework. And so I think if we have a better understanding, a system of framework for understanding what AI is. I think the narratives will become more common sense. The narratives will become more pragmatic, become more balanced. We want to keep people safe. But one of the best ways to keep people safe is that advancing a technology quickly. And I think the industry is doing that. I'm very proud of the industry for doing that. No one wants to drive a car from the first decade of cars. No way. I think ABS is a really good thing. Yes. ABS is a really good lanekeeping is a really good thing. There's no question. FSD is a really good thing. And I think people will be excited about the third or fourth year of AI. Yeah. No doubt. And I say with great pride that the industry made tremendous strides this last year. All the technologies we've mentioned and that the scaling laws are so intact that we now know that more compute, more intelligence. And gosh, the innovations in one sector defuses and spreads across all of the other sectors so fast, I'm so happy to see all that. And so I think the next five years it's going to be extraordinary. No doubt about it. And I think next year it's going to be incredible. Amazing. Well, we're excited to talk to you at the end of next year too. Yeah. Looking forward to it. All the work that you guys do. Congratulations. What a great year. I'm so excited. Amazing year. Yeah. Yeah. Thank you. Happy New Year. Happy New Year. Happy New Year. Happy New Year. Happy New Year. Yeah. Find us on Twitter at no priors pod. Subscribe to our YouTube channel if you want to see our faces. 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