“For us to be able to understand biology the way we understand many other fields of science will be incredible. So I think that’s probably our single greatest potential of helping. “ — Jensen Huang

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please join me in welcoming to the stage My Fabulous boss president and CEO of the bipartisan policy Center Margaret spellings and the co-founder president and CEO of Nvidia Jensen [Applause] hang all right thank you to my fabulous colleague Tanya for that warm welcome and introduction wow what a great crowd thank you all for uh being here this morning and what a treat it is Jensen to be with you I’m delighted to be here I mean we’re thrilled that you’re here I was teasing with Jensen you know who’s known for his famous black leather jacket outfit and I want wanted to be in solidarity with him today and he shows up in a suit uh the only one he has by the way but anyway um it’s the truth I’m not ashamed of it it’s a good look so uh as you know we at BBC are uh proud of the role that we play as a place to bring people together to find common ground and to drive towards Solutions and there’s no thing that needs more uh thinking good thinking around that than Ai and energy and so we’re thrilled to welcome you here today thank you Margaret and um so as everybody in the room already knows but maybe out in TV land they don’t uh Jensen is the founder in of Nvidia a 30 trillion dollar company with about 30,000 employees not quite 30 yet not quite 30 that’s what I’m sorry three you know what’s a zero well a zero is a lot in computers but anyway we’ll get into that uh anyway J we’re working on yeah welcome welcome sorry just I’m so impressed with you um anyway why don’t you tell us about how you got started give us your kind of so-called origin story and how it is that you came to be sitting with me here today my gosh from from where from zero no from you know what tell tell us the story of Nvidia Nvidia and you and uh we were in Silicon Valley engineers in Silicon Valley designing computers and we observed that there was a better way of Designing computers uh if you if you you um uh the current computer that we use it’s called general purpose computers it was invented the year after my birth 1964 by IBM it’s called the IBM system 360 and it described a central processing unit CPU uh multitasking separation of operating system separation of hardware and software by this layer called operating system IO subsystems things like that and those basic Technologies are used today it’s a lot better A lot faster but the basic architecture of it is the same it’s a general purpose computer that could do anything now we observed 30 years ago it was 1993 we observed that that there are problems that are quite specialized where a general purpose approach is not necessarily the best you know and and uh it’s no different than if you have generalist do something and it turns out that uh software is kind of the same way and there’s a field of software called simulations and physics emulations and data processing and computer Graphics these problems image processing these problems have algorithms inside that are very computationally intensive and if we could take that and run it on a specialized processor on a specialized computer we could add a chip to the computer that makes it Go a hundred times faster how is it possible to add one chip to another chip and all of a sudden it’s a 100 times faster well the reason for that is of course each chip can focus each computer can focus on what it does best and we offload and accelerate this thing and we call it accelerated Computing now uh the amazing part of it of course is that the domains that we’ve been able to use it for has been expanding over time and uh it’s not it’s very routine that we accelerate an application 10 20 50 times um and when you do that even though you accelerate it by 50 times since you only added one more chip you reduce the amount of Power by 25 25 times the amount of energy by 25 times the amount of cost by 25 times and so so that this uh this approach has led to um solving all kinds of interesting problems the first application we used it for in scientific in in in commercial use was for video games and you know for a lot of people Nvidia is still the video game company we build you we build more computers for video games than any company in the world when you have when you play PC games it’s probably Nvidia inside uh when you play the Nintendo it’s Nvidia inside and so so we’re well known for that um but some of our industrial applications the first one was molecular Dynamic simulation for virtual screening uh seismic processing for energy Discovery and then you know Floyd Dynamics weather simulation and then one day artificial intelligence found us and and so um accelerated Computing that was it was an observation about the future of computing it turned out to be right and so when you extend that into what you call sustainable Computing this is where you get into the savings of energy and being the observation that we made is is that a general purpose approach to solving every problem is energy inefficient mhm you’re inefficiently doing something it’s a little bit like this suppose suppose um uh you made a factory for building a car and the assumption that you make is that every single car that you make will be different and so you have a generalist team of people that okay build me a van okay now build me a sports car okay now build me a truck and and so because every single one is intended to be different and you want to be generalists MH you would have a different type of setup than if you all of a sudden said to yourself for the next 1 million cars they’re going to be exactly the same the efficiency by which you could build that 1 million cars compared to a generalist right that would build at one at it’s like manufacturing versus crafting right the amount of energy you use is going to be substantially lower and so we’ve come to the conclusion we came to the conclusion some 32 years ago go that there are some parts of the job that needs generalized and you need to do it sequentially just step by step by step you have to reason about it step by step however there are some parts of the job where you’re maybe calculating physics you’re just trying to figure out how molecules move around you you’re trying to figure out how artificial intelligent neural network is predicting the next token the next word in those type of applications you could have just a mountain of processing at the same time and so this doesn’t make sense to you but I’m just I’m going to say it anyways the the execution of a program is called a thread a thread of execution and just follow the follow that thread a CPU can compute one or two threads at one time we can compute tens of thousands of threads at the same time and so for for work that you can break up into a whole bunch of little threads we could get it done a lot faster and you know as a result much more energy efficient so so let’s talk more about Ai and energy I mean there’s a lot of kind of Hysteria going on right now about oh my God this this industry this Innovation is going to swamp our demand and we’re going to have blackouts and you know this those sorts of things obviously energy abundance is a you know a key issue for all of us here in the United States especially I’m from Texas so we talk about that a lot but give us your thinking on AI and and energy at an Nvidia and more and more broadly how you think about that well AI does take energy yeah and so the first thing to realize that before you can use AI you have to teach an AI this is no different than a when you before you can use an actual intelligence you have to teach the actual intelligence and so the teaching process consumes a lot of energy and and the reason why it consumes a lot of energy is that the artificial intelligence network uh through trial and error is trying to figure out how to predict something and it’s recognizing patterns and relationship among tons and tons of information and from all of that data it finds out um what’s the relationship and the pattern such that it can learn a knowledge out of it we call it representations and so you’re trying to discover knowledge out of data and just you’re just swimming in it processing it repeatedly looking for that pattern relationships eventually uh you can you can um uh you’ve learned you learned the knowledge that’s embedded inside you’ve learned um all these different relationships that when you’re presented with uh some future patterns uh you could understand it you could even predict it okay and so so that that’s the goal these data centers um could consume uh today maybe a 100 megawatts and in the future it’ll probably be uh several you know 10 times uh 20 times more more than that mhm uh it doesn’t have to necessarily be built in one place um but the amount of data that we’re going to train it with and these AI models are going to use synthetic data for example two AI models are going to be talking to each other just like the two of us are right and just through conversation through Q&A we’re going to make each other smarter and so uh future future AI models are going to rely on other AI models to learn and uh you could you could um use AI models to curate the data so that uh future AI use an AI to teach another Ai and so there’s a lot of a lot of different ways that that AI will will will learn the future but nonetheless it’s going to take energy to do so the important thing to realize about AI is that it doesn’t it doesn’t necessarily care where it learns it doesn’t care where it goes to school and so um there are places in a world where we have excess energy it’s not necessarily connected to the Grid it’s hard to transport that energy to population but we can transport the data center too we can build a data center near where there’s excess energy and use the energy there the other the other the big idea about about AI however is um the goal of AI is to do things more productively right with a lot less energy and there are a whole bunch of examples of this where we’re using AI one one of my favorite examples is is um using AI to predict weather uh by training an AI model the physics of weather prediction in the case of weather prediction is most be atmospheric physics a long range weather prediction requires you to understand ocean physics land physics Cloud physics uh radiation reflection absorption conduction convection right thermodynamics right fluid dynamics all of those forms of physics comes together um to uh to affect the future climate well we can teach an AI how to do that through previous observe data and we’re starting to make great progress there that AI model can predict whether and predict climate thousands of times tens of thousands of times more energy efficiently and so the thing to remember you teach the AI model um a few times but you use it continuously everywhere around the world and so the benefits of using AI to predict weather and instead of using supercomputers to predict weather as we do now right can save a ton of energy yeah yeah so that’s one simple example but the example just go on and on right so it’s part of the solution as we think about climate not so much part of the problem I mean it really is I mean if you there there are other examples Margaret is um for example that’s that’s one uh that using artificial intelligence to solve problems will consume less energy than using calculation to solve problems right okay and so um uh that’s number one number two we use artificial intelligence now in the smart grid you know our power grid is not smart but we can make it smart right and so we work with a company called Y data and U pg& uh to do a to create an AI computer and we put it integrated into the smart grid the goal is with uh AI with intelligence and a grid you could figure out how to uh for example exle integrate uh sustainable energy into it that sustainable energy could be from solar it could be uh the battery sitting in your car um during power surges and outages um it could recognize that there’s a weakness in a grid and figure out where the various distributed energy sources are to keep the grid going uh maybe you could predict um uh uh very subtle signals of when the grid is about to fail and um send people out for predictive maintenance yeah uh you could also uh do a better job predicting the surges the power surges that’s about to happen so you can redirect energy accordingly um so smart grids smart grids I think is going to um make sure help us not over provision in the grid right you know today as we know the grid has the ability to handle a lot more power delivery it’s just that the promise that we made the society is that you’re going to get power all the time right well unfortunately the power use in population surges right extraordinarily could be a factor of two during just a few days of the year right the rest of the time it’s kind of a lot lower but you have to provision for those days right because people need the power need the energy need the heat or cooling to stay alive and so so um uh we could we could uh redirect uh uh if the grid was smart we could redirect the energy in a smarter way and so there’s a there’s lots and lots of examples you’re a really good teacher uh so here we are in Washington and obviously we’re all about federal policy the federal government has been an important partner uh on innovating on some of the things that you’re talking about how do you see that that history your work with with the US Department of energy and then where we ought to go next I mean just give your sense of you know if you were if you running the department of energy and had checkbook you know how do you see those Investments being deployed in the smartest ways MH it’s a big checkbook too well you did mention $30 trillion exactly I’m for you yeah um uh the the thing I recommend most for for um every employee Nvidia every Company CEO that I meet um uh every lead leader that I meet is engage artificial intelligence try to have a tactical feel for it m um this mystical thing is not so mystical uh in its extreme of course uh it’s as mystical as you and I sitting here having conversations with each other and it turns out there’s there are several things that we should observe about about artificial intelligence today we’ve we have through invention of Technology the most advanced Computing technology ever created we have made the computer easier to use than ever to the point where almost anybody can talk to the computer prompt it for something ask it to do something for you including write software for you and it will write software for you and it could make drawings for you create schematics for you it could make charts and graphs it could read something for you translate something for summarize something for you solve problems for you and so this incredible thing this computer is is now democratized for the very first time in history this incredible system this incredible technology is available to just a small percentage of of the world is now available to everybody to use and the the first thing to do is is to try to understand how how you could take advantage of this technology yourself now of course once you you go down that Journey you’ll discover all the same things that that scientists and engineers and all of us are starting to discover that not only is it more energy efficient um we can use our energy better uh we could be more energy sustainable it could create uh new materials so that we could we could uh create sustainable energy more eff effectively uh some of the important work that we’re doing in carbon capture just trying to figure out in a reservoir how much pressure can we um uh pump into carbon into this Reservoir and how much how much this Reservoir has over already been saturated with carbon and so we could select the best injection Wells for carbon capture right and this simulation uh takes supercomputers just an enormous amount of calculation we’ve taught in AI how to go help us select these sites with nearly a million times less energy and so there’s a lot of different ways that we can use artificial intelligence to to help being inspired by those ideas I think would really help um doe and and of course some of the things that that I would recommend is uh invest in using it yourself you know one of the things that that I would really love to see the United States do is for the government to to become a practitioner of AI don’t just be a governor of AI be a practitioner of AI um the doe the dod you know every single Department be a practitioner and and um uh build an AI supercomputer uh the scientists would be more than happy to jump on it and create new AI algorithms you know to advance our country yeah fabulous yeah so obviously you were you know a startup innovator well many moons ago but you all uh have affinity for that as a company through your Inception partnership Network talk about that and how you see that ecosystem of of innovators well we know a lot about this technology um we have a lot of scale and resources and um uh we have we have abilities and so so when we see companies uh around the world that are uh trying to apply AI for some particular domain uh it could be a domain like for example Uli data I’m just I really love that company they’re they’re a company that wanted to uh build a smart grid but they didn’t understand artificial intelligence that well and so working together we became an expert in artificial intelligence and the application for smart grids cool uh uh we work uh uh with a company that’s uh that has satellites out in space and and um oh oh sat that’s where satellites go space where satellites um I mean even I knew that yeah well it takes it takes a CEO to tell you those things and and uh and uh and these are these are they take images of the earth and and at a at a um at a spectrum uh that is beyond uh cameras and so that we could we could use that that hyperspectral Imaging system and teach an artificial intelligence how to see you know we can’t see anything from it we teach an AI how to see something from it and discovers gas leaks and and uh Reservoir leaks and such I mean we could we could um there all kinds of things we could these companies have imaginations to do and we could you know help them with our AI capabilities and so we’re working with thousands of startups and do they find them or do you find you find them or do they find sometimes we find them sometimes we find them they find us often times I even find them so yeah but I read about them and here’s a company that’s doing some really interesting things ex you know the company that’s you know watching out for for uh uh keeping Birds safe uh in the middle of a wind farm yeah and so yeah all kinds of great ideas cool uh you won’t be surprised to that I’m going to ask you about Workforce there’s a lot of uh obviously implications to the good and and a lot of fear around what is the meaning of AI for our American Workforce or our Workforce globally obviously and how you think about that as your own human capital asset as a company and how you develop and grow but also you know the broader implications uh for the workforce first in the US but but in the world the first thing we need to do is demystify this technology for people and the reason for that is because it’s useful to them it can Empower them but not not if they don’t understand it and as it turns out it’s actually relatively easy to understand very few people in the world know how to program a computer but everyone knows that knows how to ask someone else to do something for them and it’s as simple as that yeah it’s as simple as walking up the computer and tell it you know what you want and for the first time it might even give you a reasonable answer back yeah and you don’t even need complete sentences you could be grammatically incorrect you just you know and start and if it doesn’t understand uh what you mean it’ll actually ask you back what you mean and just go back and forth until it figures out what you want and it does it for you now we have demystified we we need to demystify artificial intelligence so we can empower the population to take advantage of this amazing technology that’s only been available for a few percentage of the world in the past and so we could close the technology divide we could enable everybody to take advantage of it and so the first thing that we’ve done is we’ve uh we teach a class called Deep learning Institute and we go around the world and we uh we’re doing we’re doing um a fair large campaign in California uh in school in community colleges in high schools and such and and anybody who would like to learn uh to uh teach them about about the capabilities of artificial intelligence we should do that all over the country we should be teaching everybody um exposing people to this technology and I think it’s easy to use it’s fun to use it’s inspiring to use you can ask it to help you draw something give you a recipe you know help you write a business plan y you know all kinds of things yeah but obviously there’s no substitute for the ability to to read and and Cipher and compute and I know you all have been involved in in education programs at your Alma moders and so forth but how do you think about your own human capital and what do you look for in your in your people and your leaders is it is it you know given all the promise that AI has for the workforce you know what are you looking for as you look for and recruit leaders and people to join you in building Nvidia all the same things I look for in the past um nothing’s going to change the one thing that’s going to change is is the amount of software programming that we do the actual programming that we do will reduce or another way of saying it is the way we program computers will change because you’re just going to ask a computer to do it exactly exactly we’re going to describe very clearly what we want the computer to help us do right and the computer will do it for us so the way that we would design chips in the future would be describing probably the specifications of the chip which is telling somebody what you want is is often times where the genius is right right yeah where the genius is yeah doing it doing doing the actual skill of course there’s great craft and and um great dedication and um U but you know to to to be able to explain a future that you would like to create um and have have uh AIS that that um uh help you go do that is is pretty terrific and so my prediction is is that the first thing the first thing companies like ourselves would do is use artificial intelligence to improve our productivity that’s what we’ve done we’ve got AIS all over our companies designing our chips writing our software helping us debug things help us do verification we’re starting to work on using it for marketing and customer support and things like that and so so uh number one help us be more productive when a company becomes more productive they make more money when they make more money they hire more people and the reason for that is because we have more ideas we like to go pursue right right and so I would love for United States to be more productive because we’ll have more money and then because we have abundance of ideas we’ll be more prosperous so so I I think the the um the idea the idea that that it’s it’s human versus AI is not quite right is humans using Ai and with respect to anybody who who’s concerned about an AI taking their job you should probably worry about someone who uses AI taking your job yeah and so so that that reminds you to to get going and to go learn this uh learn this new tool yeah and to make this new tool you know your advantage and surely as a company we’re going to do that yeah and as a country we should do that and and obviously what are those skills that are necessary to be able to be precise and you know at at the telling what to to do that’s part of the the AI equation that’s right wow uh okay National Security MH obviously you know a huge you know Nexus between our nation security and and these issues talk about that how you see the world and and the National Security implications for this technology taking a step back um countries are starting to awaken uh to the importance of artificial intelligence to their country mhm on first principles it is apparent and the reason for that is simple no country in the world would say uh we should Outsource our intelligence to anybody else no country in the world should say would say we have an abundance of intelligence this is good enough let’s put a lid on this one let’s wean oursel off of [Laughter] this and so so I think I think it’s it’s now very clear that the that artificial intelligence is about the The Accelerated production of intelligence hey please remind me to talk about um artificial intelligence as an industry and the production of it I just wanted because for energy is so important um and uh uh countries are also starting to realize that the land they’re on is part of their natural resource and Sovereign resource but their language culture people their way of thinking right is part of their natural resource and it’s that is codified in data and so to to allow other countries to come in and scrape your data Harvest your data Harvest your natural resource exactly refine it and then import it back to you as artificial intelligence is unacceptable and happening yeah that’s right and so so countries are starting to realize that they have to take control of their own artificial intelligence production and this is where the word sovereign AI if you will is starting to float around uh countries in the in the west countries in the East are are quite concerned and quite motivated to secure their AI infrastructure just like they secure their their telecommunications their power grids they like nuclear assets nuclear assets they want to really secure their artificial intelligence infrastructure and so that’s that’s really one of the the major Dynamics that’s happening around the world that that they realize now the incredible potential of this technology to accelerate their uh climbing if you will you know in the world’s um various uh social ladders and so this is a a great tool for them to propel their National prosperity and and I so I think on on first principles people people Now understand uh the incredible importance of artificial intelligence for their National Security National prosperity and National development yeah and so so I think I think every country is starting to to realize that now from our United States uh we need to realize that this technology is is uh indigenous to us it was created here congratulations we have thank you and and we’re proud to be an American company and uh if not for the United States and and all of the resources that was that was made available uh in video wouldn’t be Pro wouldn’t be possible and so I I think the the the um uh uh the the country’s desire uh to uh number one protect this technology uh for our own benefit uh is fantastic and we want to use this technology of course to accelerate and Propel the United States further the important thing is to realize also that to balance that with the idea that that American Technology around the world sets American Standards all around the world right exactly we would like we it’s fantastic that the world speaks English yeah it’s fantastic that um that the world is is powered by American Technology and we would like of course uh Nvidia technology to also be used all over the world to set the pace for the world um to make sure that the American Technologies are used to build other nations and other Industries around the world and so we want to find the balance between National Security and of course um prosperity for American companies around the world and so that balance is hard to strike and um but whatever whatever the administration does of course is something will support yeah I want to ask you about regulation and and the role in that but you were going to say for a second about uh AI production this is the big observation from an energy perspective this is really important to know um for the very first time time a computer is a tool but it’s also a factory it used to be a data center where data is stored but in the future these computers that we build um will be used in a way that’s very different than the past these computers for example I have I have a phone in my pocket right now it’s a computer and it’s a tool of mine just like a Quin art just a lawn mow just like anything any other tool Mone a tool when you’re using it you’re using it when you’re not using it sitting idle my phone’s sitting idle right now however in the world of artificial intelligence there will be systems there will be AIS that are doing things for us all the time we ask it to go do something and it does it all the time just like we ask one of our employees to do something it does it all the time and and um we like it to be uh human in the loop but as autonomous as as autonomous as possible as govern as necessary and so these computers are off working on things designing chips writing software optimizing things going through plans evaluating all the various plans that we have and trying to figure out which one is the best to come back and recommend it to us and so the exploration of the design space exploration of the the the optimization Space is really really large and we want these AIS to go explore them to go look for new scientific discovery for example and so so it’s off in in these machines and it’s running all the time in a lot of ways these computers in the future will be in AI Factory right and there’s a new Factory that is being created right now there’s a new industry that’s that’s being created right now it’s called AI right and remember a new industry requires energy and this is a new addition to the past and so while accelerated Computing what Nvidia does um allows us to save a lot of energy in the way that we use the compute so every every every software that can be accelerated should be accelerated um we should modernize old data centers with the new type of computing models that are accelerated and we’ll save a lot of energy in doing that and that’s a classical data center that what that’s what we used to build but there’s a new thing that’s called AI factories and that’s going to consume energy but what of course comes out of it is artificial intelligence that will help us to save energy somewhere else right and so this is a this is a if you will an industrial revolution a new assembly line a new you used your car analogy a bit ago um regulations and guard rails and policymaking OB you know there’s a lot of energy on the hill and and rightly about how do we think about you know preserving Innovation while protecting National Security our children you know fill in the blank and our role in the world Visa the Europeans or other me how do you think about those issues as you know members of the Congress down the street here start to put pen to paper on the way forward for your industry well first to help them understand the technology all of the potential uh incredible good that it can do uh around the world uh recognizing the the uh the threats and the danger of this this technology of course um the technology is fundamentally intelligence and intelligence could be used in a lot of different ways for for great and for Peril and so and so we we uh one help them understand the state of the technology where are we today um uh where will it be in some reasonable time and how to how to think about the technology more practically instead of theoretically scence science fiction wise right and so um without without us demystifying it it’s hard for people to understand this technology and so one help people understand the technology um help people understand the the uh the very very good use that’s already coming out of it um and in various fields of science whether it’s in health care or um climate science or education education or everybody should have their own tutor um and so I have my own tutor today and my tutor is perplexity I I use it almost every day what what is this tutor perplexity perplexity perplexity it’s a great it’s a great great uh great resource um it’s an AI and uh calls upon other AIS and and um uh you could ask it all kinds of questions and it’s really really really helpful anyways it’s taught me a lot about about um about digital biology and so it’s really great so you’re obviously a half so one one uh make sure that that that we educate them about about um uh the opportunities around the world so that they understand that exporting American Technology is winning abroad and we want to win here we want to win abroad we want to win everywhere and and um all of the policy makers I’ve met wants America to win and that’s great that’s great but there is the you know the other side of the coin we hosted uh Brad Smith from Microsoft I don’t know a few weeks ago and they released a report on on AI generated content the implications for kids and others and so forth so you know how what do you what about that you know keeps you up at night piece of it the darker side of AI how do you think of that well it’s um I it’s going to take AI to catch darker side of AI MH and and the reason for that is is pretty clear you know they AI is going to be producing uh um fake fake uh uh data and and um false information at very high speeds and so take ve somebody with very high speeds to detect that and um to shut it down higher speeds higher speeds that’s right exactly and so so so I think this is no this is very similar to Cy cyber security it it you’re almost every single company in every single country is being uh haed hacked and you know attacked at almost all times right and so it’s going to take even better cyber security to defend ourselves and so I think the um uh we just have to make sure that we stay ahead and AI will help us do that it’s going to take AI to help us stay ahead yeah yeah so before we go a question want to ask you about obviously you’re wildly successful about to be admitted to the nationaly and so on and and how do you think about your you know responsibility as a citizen as a philanthropist supporting education I mean just you know kind of own that piece of your remit now how do you think about that well the most the the first thing that that um uh be a good father that that’s number one be a good father and husband uh number two I hoping you were going to get get that in there that husband pays cuzz there she is right there yeah brought my wife yeah um uh the first thing uh building Nvidia is probably the single best thing that that um any of our employees any any one of us has ever done we we’ve built what what is what is recognized one of the most consequential technology companies in history yeah uh and uh our technology is used um in groundbreaking work in so many different fields of Science and industries uh uh We’ve um uh of course uh uh invented invented Technologies over the course of three decades yeah that that um uh nobody nobody could imagine right and and um so I’m proud of that that’s probably our most important work and applying this technology uh and advancing it further for some of the most challenging and pressing issues of our time whether it’s uh digital biology or Health Care is is some of our some of our um uh best futures um I can’t imagine you what we’re going to be like in 10 years frankly yeah and when we apply artificial intelligence to um uh the field of biology and to for us to not to move Beyond calling it life sciences uh to life engineering just like we do and for us to be able to understand uh biology the way we understand U many other fields of science would be incredible and so so I think that that’s probably our our single greatest potential of helping and um and working on it yeah well congratulations you obviously have built something incredibly powerful and fascinating and you’re a learner every day I can tell already so all right we’re going to have questions from the audience and I think there’s some out uh in The Ether we have several hundred people online that are that are watching as well so hands popping up everywhere if we could get some microphones around yeah hi my name is Chris Barnard first of all thank you for everything you do as an investor from 2017 a personal thank you as well the stocks been doing very well um obviously we saw some very exciting news last week with Microsoft helping constellation reopen Three Mile Island and the power of nuclear to potentially help this AI future I wonder if you have any thoughts on how nuclear can be an integral part of this thank you nuclear nuclear is going to be uh a vital integral part of this no one no one energy source will be sufficient for the world yeah and so we’ll have to find that balance balance it’s it’s not one particular way versus others but but there are a lot of good ways and and but there’s no better way than to not waste energy and and there there are a couple of ways that that we can contribute to doing that accelerated Computing is one way and uh the reason why we’ve become so successful is because the computational energy necessary to get the work done using the Nvidia approach is orders of magnitude less than using general purpose Computing and um not wasting energy not wasting money not wasting time is probably the single best thing we can do um uh there’s a whole bunch of other ways that we could not waste energy um I would really love to see our power grid all be smart you know to today our nation’s power grid was built a long time ago because we’re one of the earliest countries to become prosperous and and that power grid could uh benefit from uh the insertion of artificial intelligence and smart technology into it and that smart grid uh that grid when becomes smart uh will help us uh properly provision technology to the right places and you know connect the right uh sources and syns like we connect the right drivers and writers you know and so in order for Uber to work you need a smart grid and and we could we can go create that smart grid exactly others we’re for the col McCormack from Georgetown University in carbon direct one of the huge challenges with renewable energy today is the interconnection queue we have thousands of gws of solar wind and batteries waiting to connect sometimes for years for studies to be completed what can tech companies like Nvidia do to speed up the interconnection queue and get that renewable energy generating faster than it is today yeah um you you know the reason why fossil fuel is so effective is because uh time mother nature compressed it into a transportable for form for us and we can take it anywhere and refine it from anywhere and um uh the challenge of course with electric energy is the battery costs a lot of money and uh and solar and sun is only out for about half of the day and and so there there are a variety of of challenges of sustainable energy in that way one way that I described earlier is instead of transporting the energy to where we need it let’s transport the data center to where there’s energy source and we put that data we can build a data center anywhere the computer the AI doesn’t care where it goes to school and and you know although we would like the AI to be trained as continuously as possible taking a nap for a couple of hours while the sun is down it’s it’s okay you know we can live with that and and so so so long as the energy is is um abundant and and there’s going to be excess anyways um I I think that is a great way to do it and we can then take that AI compress it that energy compress it into this little tiny thing called large language model and we can transport that anywhere we like to use it so others up here let’s see there we go you’re next hi Katie with Constellation Energy we actually made that announcement uh last week about bringing the nuclear plant back to congratulations thank you thank you it’s very exciting um but kind of pulling off of what you were just saying um we have heard uh we have lots more to offer right we have lots more nuclear energy in this country that can be used to serve data centers AI factories um but we’ve heard about some proposals that would require what they say is additionality for AI and this idea that if you’re going to build an AI Factory you have to build commensurate energy to go along with it and I’m just curious your thoughts on the feasibility of that and whether that’s going to allow us to really win the race on AI or if there needs to be some other policies to consider about how we can use both existing and new resources I was I was uh I was here a couple weeks ago and the administration was very clear that that um uh they would like American companies to have uh as much opportunity to build data centers here in America and and and uh the admin Administration recognizes that that um uh permitting and getting access to the power in various various places around the country could be difficult um and that they would like to be an ally to help um uh with with uh uh accelerating that process so that the American companies don’t have to look offshore and outside our country to do so and so I I think this is one building the AI infrastructure of our country is a vital National interest and and although although uh it consumes energy uh to train the models the models that are created will do the work much more energy efficiently and so when you think about the longitudinal the life the lifespan of an AI um the Energy Efficiency and the productivity gains that we’ll get from it from an industry from from from our society is going to be is going to be incredible and um uh and so we we spent some time to help people understand the big pictures of AI uh the challenges of of um provisioning energy to AI but also some of the some of the things to to uh realize that the AI is running an AI Factory and training an AI doesn’t have to be like running a hospital it doesn’t need to have 99.9 9999 99% up time you know if it’s down if it’s down a couple percent you know 5% from time to time it’s okay it’s okay you know just stop studying for a few hours you know you’ll pick up where you left off and so it’s actually called checkpoint and restart you know take a break and we’ll pick up where you left off and so so I I think um we should understand the the the challenges but also the differences challenges and the differences of provisioning uh energy to artificial intelligence that’s like this question on the screen here will AI really save energy or we just use the efficiencies to power other tasks so you’ve answered that in part but is there any way to say like how much or what our Target might be well we have many examples uh one example one example is uh the example was giving about weather simulation right right yeah we we uh predict weather 3,000 times less energy than a supercomputer mhm um the the number of examples are are quite abundant um but it doesn’t change the fact that this question is the other side of it is probably right that we’ll end up saving energy but society would then apply the energy saved to go do something else yeah and and um we call that Prosperity yeah economic grow yeah economic growth uh the Improvement of quality of life which we want to see the fact of the matter is on an absolute basis I hope that we all hope that the population of Earth consumes more energy someday because it’s directly related to quality of life it’s directly related to Prosperity we we want every everyone to enjoy this quality of life yeah and so absolutely amen all right over here yes there’s a microphone where are we we okay over here but please microphone come over here oh you’ve got one okay yes ma’am okay thank you so much thank you Mr Jensen really appreciate your commentary today and I want to touch on one of your critical points which is the importance of educating policy makers on the realities of AI so my name is Amita lazari I come from the tech policy profession previously at Intel and today I’m a startup founder co-founder of a company called open policy and we use Ai and work with some of the best unicorns in the world to better connect them to what’s coming from policy and be able to scale policy engagement a profession that has uh you know some special expertise that requires that translation and I want to ask your opinion on how AI can be used to scale uh think thank work that this connection between Innovation and policym and just liberate all of this information that is here in DC about what’s coming and bring it to C Valley you said it so fast I think my data rate is about 80% of yours and and is uh I I think I think I I I think I understand if I answer a wrong question uh let me apologize in advance the um AI has a wonderful ability to teach to explain very complicated Concepts and I use AI today to as I was mentioning to Margaret that I use it literally every day I literally use AI every single day and I use it to explain things to me and when when um uh it’s a New Concept uh I might ask it to explain it as simply as possible and then I can dig further and further and further and and uh delve further into it and and I love the fact that they could explain it as a fifth grader I love the fact that that um it can give me more depth and and I can then ask it you know explain it to me now from their first principles of Science and and um uh uh now explain it with analogies now break it down step by step you know all these different ways of learning because you know when we engage new ideas we need to engage it at different levels and ideally you know at the highest possible level and break down the information you know into its Elemental Parts over time and so I I do think that that uh the work that you’re doing using AI um to expose the Technologies to policy makers would be a wonderful benefit to them now in the future hopefully they’ll just use it themselves you know just it’s on the phone just you know which is the way I use it and and so it’s like my my phone now is is super smart yeah but the ability to query in a intelligent and useful way is certainly a skill that you obviously have sir hey Jensen thanks for coming here Mike Chan from Deep Ventures um first of all you and my wife share a last name so we should talk about that sometime um okay uh second of all I invest in if you’re suggesting I owe you money check my I um I’m just kidding uh so I uh invest humor is allowed in DC abely this is my first meeting a the course correct immediately um so I invested in crypto startups and our industry would kind of like to thank your industry for taking a little bit of the heat off of the negative PR of Bitcoin mining and and all that but but that that being said um you know a lot lot of power producers energy producers um the excess energy they allocate that to Bitcoin mining because there’s a very clear Market there um kind of onetoone financial Market there do you see something like that possible in terms of like a smart Grid or like having those companies be able to dedicate some of that excess energy to training models and like and providing that power to you just gave um uh a an a current example of excess energy being used to convert in to store that energy essentially what Bitcoin is doing is taking excess energy storing it into a new form it’s called currency and you take that currency and you take it wherever you like and so you took energy from one place and now you’ve transported it everywhere now of course that’s just Bitcoin imagine a much more Universal currency called intelligence using exactly all the same concept that you described you you find places with excess energy go put a data center there trans transfer that energy compress it into an artificial intelligence model take that model all over the place to use it make sense same idea excellent okay we’re going to ask this question and then one more it says can you discuss the difference in requirements for hydrogen production with AI versus without I have no idea I was going to say I mean ask ask your phone to answer this this question all right thank you for that though okay all right okay last question yeah hello I’m a lawyer from South Korea I’ve observed how MD AI technology are revolutionizing various industrious in the legal sector the adoption of AI is accelerating particularly in area like document analysis Automation and Regulatory Compliance monitoring which hold significant Potential from your perspective how do you envision AI te technology being utilized in the legal and Regulatory Industries in the future and what Innovations might we expect to see in these fields thank you regulatory implications of AI regulatory impc ation of AI um there will be no task done no knowledge task done in any industry by anyone that will not involve AI in the loop in the near future any information knowledge worker it could be information about an engineer um legal documents and software code reads similarly if well written and I AI contributes to to software coding significantly today we use AI to code write our software inside our company we use AI to debug our software which is to find the flaws in it you will use AI to produce legal documents you will use AI to analyze documents you will use AI to enhance the legal documents and so uh every aspect of of information knowledge will involve that therefore all policy will involve AI in the future and the reason for that is policy is code it’s code for for appropriate behavior and code um uh if if written well um uh doesn’t contradict itself and and um achieves the mission the the goal of of uh the policy and so uh policy light code will also en be enhanced with AI uh almost everything that we do in the future will have ai in the loop and and human in the loop you know we’ll be collaborating uh to to do everything that is the perfect note to end on Jensen I think so let join me in thanking this fantastic thank you thank you thank you [Applause]

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