Cisco AI Summit | Special live event with Jensen Huang

Hey.

Yep.

Back here.

out.

Hey Heat.

HEAT.

HEAT. HEAT.

HEAT. HEAT.

HEAT. HEAT.

HEY, HEY HEY.

>> [music] >> HEY, [music] HEY, M.

>> [music] >> HEY, HEY, I'm a >> [applause] >> I feel like I'm drinking one's job.

[laughter] >> Jensen reminded me as we brought a glass of wine out here. He said, "You realize you're streaming this, right?"

[laughter] >> Hey, whatever. It's late.

>> Well, >> so, uh, >> the first principle is do no harm.

>> Do no harm. Yeah. Yeah.

>> And recognize how blessed you are.

>> Yes.

>> So, uh, first of all, thanks everybody for being here for an incredibly long day. We started this thing early this

day. We started this thing early this morning and, uh, we had speaker after speaker after speaker after speaker and then we had about a two and a half hour

break and they came back to see you. So,

uh, >> I've been up since 1:00 at >> So, this guy, [applause] this guy is on the tail end of a two week trip

and four or five different cities in Asia. Uh,

Asia. Uh, >> one day ago was in Taiwan. Last night I was in Houston. Here I am. [laughter]

>> But he's been gone two weeks and we're standing [clears throat] between him and his his personal bed versus a hotel. So,

we're gonna >> we're going to have fun and then we're going to we're going to get him out of here. So, uh

here. So, uh >> but uh you don't you don't need much of an introduction, but thank you for being here, man. We [clears throat] really

here, man. We [clears throat] really appreciate it.

>> Thanks for our partnership and really proud of you guys.

>> So, let let's let's start with uh let's start with that. We we have had a partnership and you you introduced this whole concept of AI factories and we're working on this together. It's probably

not going as fast as either one of us would like in the in the enterprise space, but can we start by talking about what what do you what is an AI factory to you? Is that so? First of all,

to you? Is that so? First of all, remember we're reinventing computing for the first time in 60 years. What used to be explicit

years. What used to be explicit programming, right? We wrote the

programming, right? We wrote the programs and the variables that's passed through APIs and are very explicit to

implicit programming. You now tell the

implicit programming. You now tell the computer what your intent is and it goes off and and it figures out um how to solve your problem. So from explicit to

implicit uh from general purpose computing basically calculation to artificial intelligence the entire computing stack has been reinvented. Now

people talk about computing u where the processing layer is which is where we are but remember what computing is there's computing there's the processing

but there's storage networking and security all that is being reinvented as we speak and so the first part the first part is we need to

develop AI to a level and we'll talk about that we need to develop AI to a level that is useful to people And until now, uh, chatbots, where you

give it a prompt and it figures out what to tell you, um, is interesting and curious but not useful.

>> Helps me finish crossword puzzles sometimes.

>> Yes. And and, uh, but only only on things that it had memorized and generalized. So if you look go back in

generalized. So if you look go back in the beginning of I mean it's a little little literally only three years ago when chatbt emerged uh that that we

thought oh my gosh it's able to generate all these words it's able to to create Shakespeare um but it's all based on things that it memorized and generalized

and but we know that intelligence is about solving problems and solving problems is partly about knowing what you don't know uh partly about reasoning ing uh how to solve a

problem you've never seen before.

Breaking it down into elements that you know how to solve very easily so that in its composition

that you're able to solve problems that you've never seen before and um uh to come up with a strategy what we call

plan to to perform in a task. ask for

help, use tools, do research, so on so forth. These are all fundamental things

forth. These are all fundamental things that now in the in the in the in the phrasiology of agentic AI, you've heard,

isn't that right? tool use, research, uh, uh, retrieval, augmented generation, which is grounded on facts, um, memory.

These are all things that that all of you in the in the context of talking about agentic AI, uh, you're starting to hear. But the important thing the

hear. But the important thing the important thing is in order to evolve from general purpose computing which is explicit programming we wrote in forran we wrote in C we wrote in C++

>> cobalt >> to that's right >> that's good stuff that's good stuff Chuck that's good stuff >> it's my fall it's my fallback job >> that's good stuff that's good stuff

>> yeah that's one of those that's one of those skills that remains valuable >> I know >> yeah I know >> that it remains valuable >> I've got a lot offers.

>> Dinosaurs are valuable forever.

>> We just established that you're older than me.

>> I know. And I'm

I'm the prehistoric. [laughter]

>> It doesn't appear so, but it's true.

[cheering and applause] >> All right, that was pretty good.

I'm the only Probably the oldest person in this room.

So how do you so let's talk a little bit about like as you as you think about the >> so so here we are uh I went to Chuck and I say hey listen uh we need to reinvent computing and Cisco's got to be a big

part of it and so we've got we've got um uh we have a new new whole computing stack coming out Vera Rubin >> and uh Cisco is going to be ting market with us on on that and so that the

computing layer but there's also the networking layer And um Cisco is going to uh integrate uh AI networking technology from us but put

it into the Cisco Nexus plane control plane so that so that uh from your perspective you're going to get all the performance of AI but in the controllability and security and the manageability of Cisco

>> we're going to do the same thing with security and um and so each one of these pillars has to be reinvented so that so that enterprise computing could take advantage of it. But the but but

ultimately and we'll come back to this hopefully that that um you know why is it that enterprise AI wasn't ready three years

ago and why it is that you have no choice but to get engaged as quickly as you can. Okay. Don't don't fall behind.

you can. Okay. Don't don't fall behind.

I think there's you don't have to be the first company to take advantage of AI but don't be the last.

>> Yeah. Mhm. So if you're an enterprise today, what what's your recommendation on the first, second, third step they should take to begin to get ready?

>> Well, I get questions like things like ROI and and um I I wouldn't I wouldn't go there. And the reason for that is

there. And the reason for that is because because um with all technology deployments in the beginning, it's hard to put into a spreadsheet

um the ROI of of a new tool, a new technology. Um but what I would do is I

technology. Um but what I would do is I would go find out what is the single most what is the essence of my company?

What's the most impactful work that we do in our company? Don't don't mess around don't mess around with with peripheral stuff. I mean, in our

peripheral stuff. I mean, in our company, we have we just let a thousand flowers bloom. We the number of

flowers bloom. We the number of different AI projects in our company is it's out of control and it's great. It

notice I just said something. It's out

of control and it's great. Innovation is

not always in control. If you want to be in control, first of all, you got to seek therapy.

But second, it's a it's a it's an illusion. you're

not in control. If you want your company to succeed, you can't control it. You

want to influence it, you don't can't control it. And so I think number one,

control it. And so I think number one, um, too many people want too many companies I hear, they want it, they want it, they want us explicit. They

want it specific. They want demonstrable ROI.

And, you know, showing the value of something worth doing in the beginning is hard.

Um, but what I would do, what what I would say is that let a thousand flowers bloom. Let people experiment. Let the

bloom. Let people experiment. Let the

people experiment safely. And we're

we're experimenting with all kinds of stuff in the company. We use Anthropic, we use Codeex, we use, you know, we use Gemini, we use everything. And and when a when one of our group says I'm

interested in using this AI, my first answer is yes. And I ask why instead of why then yes. I say yes, then why. And

the reason for that is because I want I want the same thing for for my company that I want for my kids. go explore

life.

They say they want to try something. The

answer is yes. And then they say how come? You don't go prove it to me. Prove

come? You don't go prove it to me. Prove

to me that doing this very thing is going to lead to financial success or some happiness someday. Prove to me. And

until you prove it to me, I'm not going to let you do it. We never do that at home, but we do it at work.

Do you know what I'm saying?

>> Yeah. It makes no sense to me. And so

the way that we we treat AI and and and whether it's AI or the internet before or cloud before, just let a thousand flowers bloom. And then at some point,

flowers bloom. And then at some point, you have to use your own judgment to figure out when to start curating the garden >> because a thousand flowers bloom makes

for a messy garden. But at some point you have to start curating to find what's the best approach or what's the best platform what's so that you could put all your wood behind one arrow. But

you don't want to put all your wood behind one arrow too soon.

>> You pick the wrong arrow. So let

thousand flowers bloom. At some point you curate. And so I haven't started

you curate. And so I haven't started curating yet just to put in perspective.

I've got a thousand flowers bloom everywhere. But I encourage everybody to

everywhere. But I encourage everybody to try. However, I know exactly what is

try. However, I know exactly what is most important to our company. Of

course, I of course I do. What is the essence of our company? What are the most important work of our company? And

I make sure that I've got a lot of expertise and a lot of capability focused on using AI to revolutionize that work. In our case, chip design,

that work. In our case, chip design, software engineering, system engineering. Notice you might have

engineering. Notice you might have noticed that that that we partnered with Synopsis and Cadence and Seammens and today DO so that we could insert our

technology and infuse as much technology as they want. Whatever they want, whatever they need, I will provide

so that I could revolutionize the tools by which we use to design what we do. Mhm.

>> We use synopsis everywhere. We use we use cadence everywhere. We use seammens everywhere. Use the so everywhere. I

everywhere. Use the so everywhere. I

will make sure that they have a,000% of whatever they want so that I have the tools necessary so I could create the next generation. And so so that tells

next generation. And so so that tells you something about how I my attitude about about uh what's most important to me and what I would do to revolutionize my own work.

Think about think about think about what AI does.

AI reduces the cost of intelligence or create the abundance of intelligence by orders of magnitude. That's another

way of saying what we used to do that takes you know one unit of time. Now

what what we used to to take a year could take a day now.

What we used to take take a year could take an hour. It could it could be done in real time. And the reason for that is because we are in the world of abundance.

Moore's law, goodness gracious, that was slow. That's like snails. Remember

slow. That's like snails. Remember

Moore's law was two times every 18 months, 10 times every 5 years, a 100 times every 10.

Okay. But where are we now? A million

times every 10 years. In the last 10 years, we advanced AI so so far that engineers said, "Hey, guess what? Why

don't we just train an AI model on all of the world's data?" They didn't mean, "Let's just collect all the the data from my disc drive. Let's just let's

pull down all of the world's data and let's train an IM model." That's the definition of abundance.

The definition of abundance is you look at a problem so big and you say, you know what, I'll do it all. I'm going to cure every field of disease. I'm not

going to just do cancer. Are you kidding me? That's insane. We'll just do all of

me? That's insane. We'll just do all of human suffering.

That's abundance.

Um, when I think about engineering, when I think about a problem these days, I just assume my technology, my tool, my instrument, my spaceship is infinitely fast.

How long is it going to take for me to go to New York? I'll be there in a second.

So, what would I do different if I can get to New York in a second?

What would I do different if something used to take a year and then now takes real time? What would I do different if

real time? What would I do different if something, you know, used to weigh a lot and now it's just anti-gravity?

And so, you approach everything with that attitude. When you approach

that attitude. When you approach everything with that attitude, you are applying AI sensibility. Does that make sense? For example, there are many

sense? For example, there are many companies that we're working with where the graph analytics, the dependency, the relationships and dependencies that you know these graphs, they have so many

edges, so many nodes and edges, trillions of them. Back in the old days, you would you would process a graph, small pieces of it. These days, just give me the whole graph.

How big is it? I don't care.

That sensibility is being applied everywhere. If you're not applying that

everywhere. If you're not applying that sensibility, you're doing it wrong.

If speed matters, not at all. You're at

the speed of light. If mass is you're at zero weight, zero gravity. If you're not applying that logic, if this something is not insanely hard to you in the past

and you go, "Ah, doesn't matter."

If you're not applying that logic, you're not doing it right. Now imagine

you apply that logic, that sensibility to the hardest problems in your company.

That's how you're going to move the needle. And that's how they all think.

needle. And that's how they all think.

Now the people who are If you're not thinking that way, just all you have to do just imagine your competitors thinking that way. If you're not thinking that way, just imagine a company who is about to get founded is

thinking that way.

It changes everything. And so I would go find where are the most impactful work in your company. Apply infinity to it.

Apply zero to it. Apply the speed of light to it. And then ask Chuck how to make that happen. [laughter]

>> No, let's talk about how to make that happen. So you have this analogy of

happen. So you have this analogy of >> Just call me. I'll

>> We'll call you.

>> We'll do it together. We'll do it together. you you have this analogy this

together. you you have this analogy this five layer cake because everybody's talking about like infrastructure models apps I mean how do I how do I go about it talk about that a little bit >> well the first you know one of the

things that that successful people do is they reason about what is something you know what's what's happening here so

so almost 15 years ago um an algorithm uh was able to with two engineers um solve a computer vision problem.

Computer vision is basically the first part of intelligence perception.

Intelligence is perception, reasoning, planning.

Perception, what am I? What what's going on? What's my context? Reasoning. How do

on? What's my context? Reasoning. How do

I reason about how do I compare this to my goals? And then

three, come up with a plan to solve that to achieve that. Okay. And so that's so you know for example the jet fighter problem you know perception localization

and then and then action. And so so intelligence is about those three things. You can't have the second and

things. You can't have the second and third part without perception. You can't

understand you can't figure out what to do without understanding context. And

context is highly multimodal. Sometimes

it's a PDF sometimes is a spreadsheet.

Sometimes it's information. Sometimes

just you know senses and smells. You

know what what where are we? What are we doing here? who's the audience, you

doing here? who's the audience, you know, so on and so forth. Reading the

room, you know, so on so forth, right?

And so that's about that's about perception. And so about about 13 14

perception. And so about about 13 14 years ago, we made a huge gigantic leap in computer vision, which is the p the first layer of the perception problem.

And it was super hard, you know, how do you solve computer vision? and Alex Net and the first um the first breakthrough that we saw. It was kind of like the the

first contact, you know, I love that movie, the first contact, you know, it was like our first contact to AI and and the thing that we did was we said, okay,

what does that mean? How is it possible that two engineers was able to overcome the algorithms that were that we worked

all of us worked on for some 30 years?

you know, and Ilia Suscober, I talked to him yesterday, and and Alex Kashevsky and and uh how is it possible two kids with a couple of GPUs solve this

problem? What does it mean? And so, we

problem? What does it mean? And so, we broke it all down and I reasoned about it a decade ago and I came to the conclusion that in fact most of the hard

problems in the world that can be solved can be can't be solved can be solved this way. And the reason for that is

this way. And the reason for that is most of the hard problems in the world, most of the most of the valuable problems have no no principled algorithms. There's no F= ma. There's no

Maxwell's equation. There's no

Schroingers equation. There's, you know, there's no Ohms law. There's no, it just doesn't exist.

There's no law of thermodynamics.

It's not that specific. most of the valuable things that we call intuition and wisdom and it's all you know the problems that you know you

Chuck that the type of problems that you and I get the answer is it depends do you know what I'm talking about you know if it was if it was three it'd be great it was

3.14 it'd be fantastic okay those are those are the great ones but most of the hard problems in life most of the valuable problems in life are it depends because it depends

depends on the context. It depends on a circumstance context and so so 12 years ago 13 years ago something like yeah

um computer vision was solved and so we reasoned that in fact this could be scalable because of deep learning and you can make the models larger and larger

and there was only one problem we had to solve which is how do we train that model and the big breakthrough was self-supervised learning or unsupervised

learning self AI is that goal and learns by itself. And and notice today we're

by itself. And and notice today we're not limited by labeling anymore.

[clears throat] We're not limit not even close. And so that breakthrough

close. And so that breakthrough opened up the floodgates for us to scale these models from a a few hundred parameters, a few hundred million parameters to billions

to trillions. And the amount of

to trillions. And the amount of knowledge we can codify, the number of skills we can learn algorithmically,

you know, really largely exploded. But

the basic approach was the same. And we

reasoned that in fact, we're going to reinvent and is the beginning of our conversation. We're going to reinvent

conversation. We're going to reinvent computing altogether from from explicit programming to a new way of of doing computing where the models the software

will be learned. Now what happens what does that mean if you take another step back and you go okay what does that mean to the computing stack? What does it mean to what does it mean to how you develop software? What does happen to

develop software? What does happen to the engineering organization in your company? What happens to the to the

company? What happens to the to the product marketing team that specifies the product? What happens to the

the product? What happens to the engineering team that codifies the product? What happens to the the QA team

product? What happens to the the QA team that evaluates the product? What do

these products even become someday? How

do we deploy the product? How do we keep it up to date? If you're learning it in if it's based on machine learning, how do you keep it refreshed [clears throat] forever? Um how how do you patch

forever? Um how how do you patch software? And so how do you you know so

software? And so how do you you know so on so forth? The number of hows I asked about about the future computing, you know, must have been a thousand questions and and I came to the

conclusion, our company came to the conclusion that this is going to change everything. And so we pivoted the whole

everything. And so we pivoted the whole company based on that core belief.

Simplistically, what Chuck is saying is that we came from a world where everything

was pre-recorded.

The software that Chuck worked on.

>> Really good stuff.

>> It It ran a very long time. Just for the record, >> it was indeed it was it was it was described in the Hebrew.

[laughter] >> That is true.

>> That was another skill. I mean,

>> the only person in the room that knows Hebrew cobalt and so anyways anyways [laughter] anyways anyways that that was pre-recorded. We engineer we described

pre-recorded. We engineer we described our algorith. to describe our thoughts

our algorith. to describe our thoughts and then we put we put data that goes along with it. It's everything is pre-recorded. The reason why it's

pre-recorded. The reason why it's pre-recorded, the reason why you know software in the past was pre-recorded is because it came in a CDROM.

>> Isn't that right?

>> Yes.

>> It was pre-recorded.

Okay. What is software now? because it's

contextual dynamic >> and every context is different and every time everybody who uses the software is

different and every prompt is different and all the and the pre the precursor you give it the priors you give it the

context is different. Every single

instance of the software is different which is the reason why the amount of computation necessary in the past which is pre-recorded is called retrievalbased.

All you have to do is check yourself.

When you use your phone you touch something it went and retrieved some software some files some images and brought it to you.

In the future, everything is gonna be generative just like is happening right now. This

conversation has never happened before.

The concepts existed before. The priors

existed before, but every single word in this sequence has never happened before.

And the reason for that is obviously we're four wines in >> cobalt and Hebrew have never come out of the

>> cold brew. Yes. Cobalt, Hebrew. No.

>> Thank goodness this is not on campus >> or being streamed.

>> Yeah. Yeah.

>> All right. Let's let's let's >> Do you do you understand what I'm saying? And so as a result as a result

saying? And so as a result as a result >> Do you understand what you're saying?

[laughter] The only thing that Chuck has fed me today so far is four glasses of wine.

And >> to be fair, I only fed you. I fed you one of them. You took the other three off the buffet.

>> I was I was eyeing the food. I was like, "So, I'm so hungry. I'm eyeing the food." It was

food." It was forever about 40 feet away from me. It's

>> cuz you were taking photos.

>> But it was I was like, it was so close.

It was so close. [laughter]

And I I actually leaned towards the food one time, but I was pushed back again.

[laughter] >> You know what? You know what happened?

Your team your your team actually told us ahead of time, if you get three glasses of wine in, he's optimal.

>> If you get the fourth one in, it's going to be incredible.

>> This is suboptimal. So anyways, anyways, anyways listen listen listen listen.

So what is AI?

We have to leave some wisdom behind.

>> Can we get another glass of wine, please?

>> This is not >> This is not just Dave Chappelle stuff.

>> Okay, let's talk about something. Let's

talk about one other thing.

>> Energy.

>> That chips.

>> Energy sounds good.

>> Energy, chips, infrastructure, both hardware and software. Then the AI model. But the

software. Then the AI model. But the

most important part of AI is applications.

every single country, every single company, all that layer underneath is just infrastructural stuff. What you

need to do is apply the technology. For

God's sakes, apply the technology. A

company that uses AI will not be in peril. It's the company who, you know, you're not going to lose it.

You're not going to lose your job to AI.

You're going to lose your job to someone who uses AI.

>> So, get to it. That's the most important thing. Yeah.

thing. Yeah.

>> And call Chuck as soon as possible.

>> You call me, I'll call him. Yeah. Got

it.

>> So, we don't have a lot of time, so I'm not sure.

>> We got all the time in the world.

>> Do we?

>> How much?

>> Look, look, Chuck. Chuck like he runs.

He builds on the clock. I don't even wear a watch.

>> Look at that.

>> Look at that. Chuck,

>> I got you right here.

>> Yeah. Yeah. We're doing great. You build

people on the clock. Oh, yeah. Not me.

I'm not leaving until value's delivered.

[applause] >> See, >> if it takes all night, I'm not Hey, look, I'm going to torture all of you until >> Jensen. That's why guys like me need a

>> Jensen. That's why guys like me need a watch. [laughter]

watch. [laughter] >> All right. Can you Can you >> Until you could say that you learned something, you're you are going to be trapped in here.

>> Yeah.

We're going to torture everybody until value is delivered.

>> I did check there is more wine. Um,

can you just give us your top of mind on physical AI?

Remember what remember what software is?

Software is a tool.

There's this notion that the tool industry is in decline and will be replaced by AI.

You could tell because there's a whole bunch of software companies whose stock prices are under a lot of pressure >> because somehow AI is going to replace them. It is the most illogical thing in

them. It is the most illogical thing in the world and time will prove itself.

Let's just give it let's give ourselves the the ultimate thought experiment.

Suppose we are the ultimate AI artificial general robotics. The

ultimate AI the physical version of us.

You could of course solve any problem because you know you're humanoid. You

could do things. If you were a human or robot, would you use a use a screwdriver or invent a new screwdriver? I would

just use one. Would you use a hammer or invent a new hammer? Would you use a chainsaw or invent a new chainsaw? It

just don't.

First of all, ideally they don't use it at all. But but do you understand what

at all. But but do you understand what I'm saying? If you were a human or

I'm saying? If you were a human or robot, artificial general robotics, would you use tools or reinvent tools?

The answer obviously is to use tools.

And so now do the digital version of that.

If you were a artificial general intelligence, would you use the tools like Service Now and SAP and Cadence and Synopsis or would you reinvent a calculator.

Of course, you would just use a calculator.

That's the reason why the latest breakthroughs in AI is what? Tool use.

Because the tools are designed to be explicit. There are many problems in our

explicit. There are many problems in our world where F equals MA. Please could

you please not come up with another version? [laughter]

version? [laughter] >> FA is not kind of MA.

>> It's just [clears throat] MA.

Do you guys Oh, V equals IR. It's not

kind of IR.

You know, approximately IR, statistically IR, it is IR. Okay, do you understand what I'm saying? And so, so I I think we want the artificial general

robotics, artificial general intelligence to use tools.

Well, that's the big idea.

I think that that in the next generation of physical AI, we're going to have AIs that understand the physical world, understand causality. If I tip this

understand causality. If I tip this over, it's going to tip all of that over. They understand the concept of a

over. They understand the concept of a domino. Just the concept of a domino,

domino. Just the concept of a domino, notice a child understands if you tip that over, the concept of the domino is extremely

in it's like deeply profound.

the co causality, contact, gravity, mass, all of that is integrated into a domino. Tipping dominoes over. The idea

domino. Tipping dominoes over. The idea

that you could have a little tiny domino, tip a larger domino, tip a larger domino, tip a larger domino to the point where there's a ton on the other side, a child has no trouble with

that concept. A large language model

that concept. A large language model will have no idea.

And so we have to teach, we have to create a new type of physical AI. Well,

what's the opportunity?

So far, the industry that Chuck and I have been part of is about creating tools.

We have been in the screwdriver hammer business.

Our entire life has been about creating screwdrivers and hammers. For the first time in history, we are going to create what people call labor, but augmented

labor.

Give you an example. What is a self-driving car? What's a digital

self-driving car? What's a digital chauffeur?

What's a digital chauffeur valued at?

A lot. A lot more than the car. And the

reason for that is because in the lifetime of the digital chauffeur, the economics of the dig digital chauffeur is a lot more than the car.

For the very first time, we are exposed to a TAM that is 100 times larger. Literally mathematically true.

larger. Literally mathematically true.

The IT industry is about a trillion dollars, right? Or so plus or minus a

dollars, right? Or so plus or minus a couple. And yet the e the economy of the

couple. And yet the e the economy of the world is about hundred trillion dollars.

For the very first time, we're going to be exposed to all of that. So it is the it is the case that all of you all of you everybody in in this room

today you have the opportunity to apply this technology to become a technology company.

Let me give you some examples. I really

believe as much as I look I love Disney and we I love working with Disney. I'm

pretty sure they'd rather be Netflix.

I love Mercedes. I came in a Mercedes. I

am certain they'd rather be Tesla.

I love Walmart.

I am certain they'd rather be Amazon.

Do you guys agree so far? Am I three for three?

>> All of you are that way.

I believe that we have an opportunity to help transform every single company into a technology company. Technology first.

Technology first.

Technology is your superpower and the domain is your application versus the other way which is the domain is who you are

and you're seeking for technology. And

the reason that's so, the reason that's so is because companies who are technology first, you're dealing with electrons, not atoms. And electrons, there's a lot more of

them.

Atoms, you're limited by mass, which is the reason why the moment they went from CDROMs to electrons, the value

of the company exploded by a thousand times.

you need to be like us, an electron, an an electronics company, electron company, which is another way of saying a technology company. And so I I think

that that the opportunity for you is here. Another way to think about that is

here. Another way to think about that is AI and we just said it earlier.

Even Chuck, who only knows how to program in Hebrew, >> [laughter] >> It's a gift.

His instrument choice is a right to left [laughter] because as you know it smears otherwise.

>> It is pretty smart actually.

>> Smart people do smart things.

>> Yeah.

And so so the beautiful thing is that as you know the programming language of the world and for all of your companies you kind of feel like oh my gosh you know

software is not our strength but knowledge intuition domain expertise is your strength. Well you get to you now for the first time can explain

exactly what you want to a computer in your language. Do you remember where we

your language. Do you remember where we started from explicit programming to implicit programming?

For first time in history, you could program a computer implicitly.

Just tell it what you want. Tell it what you mean and the computer will write the code because coding as it turns out is just typing.

And typing as it turns out is a commodity.

And that's the great opportunity for you. All of you could be levitated above

you. All of you could be levitated above the atomic limitations that you were limited by before. All of you could escape from this limitation which is we

don't have enough software engineers because as it turns out typing is a commodity. And all of you have something

commodity. And all of you have something of great value which is domain expertise to understand the customer, understand the problem. And that is the ultimate

the problem. And that is the ultimate value.

That is the ultimate value to understand the intent.

You know, as you know, when you graduate from software software, when you graduate from college, you could be a super programmer, but you have no idea what customers want. You have no idea

what problems to solve. But that's what all of you know. You know what customers want. You know what problems to solve.

want. You know what problems to solve.

The coding part of it is easy. just tell

the AI to do it. And so that's your superpower. So Chuck and I are here to

superpower. So Chuck and I are here to enable you to do that.

That closing was done with five glasses of wine in me. [laughter]

So >> hey, listen. as it's a miracle indeed >> between this is somebody who works off a table true representation of artificial

intelligence [applause] >> maybe that's enhanced I just want to tell you that that it's a great pleasure working with all of you um Cisco as you know

has extreme extreme expertise and two very important pillars of the invention of computing.

Without Cisco, there is no modern computing. One of them is of course

computing. One of them is of course supports networking and the other one's security. And those both of those

security. And those both of those pillars have been reinvented in the world of AI. And the part that we know very well which is the computing part of

it in a lot of ways is a commodity and the the the stuff that that Cisco knows is deeply valuable and between the two of us we're going to you know we'll be delighted to help all of you uh engage

the world of AI. And then somebody asked me earlier and just I just said you know I think it's worth repeating. Somebody

asked me earlier, should you do should you do just rent the cloud or should you even make the effort to to u uh build your own computer? Here here's what I I

would tell you. I would advise you to do exactly the same thing I advise my children. Build a computer.

children. Build a computer.

Even though the PC is everywhere, even though it's mature, even though technology is developed, for God's sakes, build one.

know why all the components exist. If

you were to to to uh be in the world of automotive, the automobile industry, the transportation industry, don't just use Uber. For God's sakes, lift the hood,

Uber. For God's sakes, lift the hood, change the oil, understand all the components. For God's sakes, understand

components. For God's sakes, understand how it works.

It is vital. This technology is so important to the future. You must have some tactile tactile understanding of it. Lift the hood,

change the oil, build something. Doesn't

have to be large.

Build something. You might discover you're actually insanely good at it.

You might discover that you need that skill. You might discover that the world is not about all rent versus all own. that you want to rent

some and own some because some part of your company should be built on prem.

For example, sovereignty and proprietary information and just you just you're not comfortable you're not comfortable sharing your questions with everybody.

You know the reason why I've never I this is a conceptual example.

You know that when you go see a therapist, you don't want the questions to be online.

>> [laughter] >> you know, you know what I'm saying?

Okay, I'm just >> I'm imagining this one. [laughter]

>> Okay, hypothetically.

>> And so, hypothetically, I I think that a lot of questions you have, a lot of conversations you have, a lot of dialogue, a lot of uncertainties you have ought to be kept private.

Companies are the same way. I am not confident. I am not secure about putting

confident. I am not secure about putting all of Nvidia's conversations in the cloud, which is the reason why we built it locally.

We've built a super AI system locally because I'm just not confident to share that conversation because conversa my as

it turns out the most valuable IP to me is not my answers. It's they're my questions. Are you following me? My

questions. Are you following me? My

questions are the most valuable IP to me.

What I'm thinking about are my questions. The answers are a commodity.

questions. The answers are a commodity.

If I simply knew what to ask.

I'm identifying what's important. And I

don't want people to know what I think is important.

And I want that to be in a small room. I

want that to be on prem. I want that to be by myself. And I want to create my own AI. And then one last thought

own AI. And then one last thought since it's already 11 o'clock.

[laughter] One last thought. There was an idea that AI should always have have human in the loop. It's exactly the wrong idea. It's

loop. It's exactly the wrong idea. It's

backwards.

Every company should have AI in the loop. And the reason for that is because

loop. And the reason for that is because we want our company to be better and more valuable and more knowledgeable every single day. We never want to go

backwards. We never want to go flat. We

backwards. We never want to go flat. We

never want to start from the beginning.

Which means that if we have AI in the loop, it will capture our life experience.

Every single employee in the future will have AI, lots of AIs in the loop. And

those AIs will become the company's intellectual property.

That's the future company. And

therefore, I think it sensible for all of you to call Chuck immediately.

>> I called Jensen. [laughter]

>> Anyhow, that's my closing.

>> Listen, let's uh two weeks on the road.

Jensen flew here, spent his last night, last evening with us before he gets to sleep in his bed for the first time in a long time. We're forever grateful.

long time. We're forever grateful.

Appreciate you being here. Thank you.

>> Thank you very much. And I I [applause] >> Thank you, man.

>> And and and [applause] from the corner of my eye, there were all these skewers.

>> Somebody was still there.

>> Where's the bag of Fritos?

>> [laughter] >> All right, let's go. Thank you. Thank

you everybody.

>> Thank you for Hey, hey hey.

Heat.

Heat.

Hey,