It could be electricity.

It could be an airline ticket.

It could be anything you choose.

We reduced it in the last 10 years by 1 million times.

Well, when something happens, when the cost of something reduces by a million times, your habits fundamentally change.

How you think about computing fundamentally changed.

That is the single greatest contribution NVIDIA ever made.

I totally see your point.

Some of our professors here may slightly disagree because you still need a lot of money to buy your GPUs.

But I come back to this point later.

Imagine a million times higher.

That's right.

But Jensen, you know.

I gave you a million times discount in the last 10 years.

It's practically free.

Ha, ha, ha, ha, ha, ha, ha, ha, ha, ha, ha, ha, ha, ha, ha, ha.

Jensen, should we turn around?

Should we turn around?

Turn around, please.

Oh!

I am alumni of HKUST.

Yes!

Yes!

Jensen, it's just so nice to have you here at HKUST and in Hong Kong.

Let me just say, can we just please show how much we love Jensen?

Yeah!

Thank you.

Thank you.

I love you, too.

Jensen, you know, I have been preparing for this moment.

I thought it's definitely a highlight of my HKUST career, for sure.

I just couldn't sleep last night for one very important reason.

Because I am going to introduce you as the number one CEO in this universe.

Maybe entire universe, at least in this universe.

But I was so worried.

This company, Apple's stock was going up last night.

And yours wasn't doing that particularly well.

And I couldn't wait until the market was closed.

And this morning, I woke up.

I asked my wife, I said, did Nvidia hang on there?

No kidding.

Because we asked.

So you are number one, Jensen.

So we have less than an hour this afternoon.

I'm just diving, Jensen, with some tough questions for you.

So you have been leading this technology field, AI field, for a long period of time.

Just for the sake of our audience, just tell us how you think about AI, more specifically, recently, artificial general intelligence, the impact AI is having and will have for all the society and the industry.

First of all, thank you.

Thank you for the opportunity to spend time with you.

Harry is one of the most consequential computer scientists of our time.

And he's been a hero of mine and many others around the world for a very long time.

And so it's a great pleasure to be here.

Harry, as you know, the transformative, the groundbreaking capability happened when an artificial intelligence network can learn to understand data of all kinds.

Of course, language, and images, and sequences of proteins, and sequences of amino acids, and sequences of chemicals.

And all of a sudden, we now have computers that can understand the meaning of words and such.

And because of generative AI, we can translate from one modality of information to another modality of information.

For example, from text to images, from text to text, from protein to text, from text to protein, from text to chemicals.

This universal, initially a universal function approximator, evolved into a universal language translator of every kind.

And so the question then is, what can we do with that?

And you see the number of startups around the world and a number of capabilities with the combination of all these different modalities and capabilities.

And so I think the really amazing breakthrough is that we can now understand the meaning of information.

Incredibly difficult information.

And so what does that mean to you if you are a digital biologist, so that you can understand the meaning of the data you're looking at, so that you could find a needle in a haystack?

What does that mean if you are, in the case of NVIDIA, a chip designer, system designer?

What does that mean to you if you're an ag tech, or climate science, or climate tech, or energy looking for a new material?

So this is really the groundbreaking thing, is that we now have the concept of a universal translator.

You can understand anything you like.

Yeah, Jensen, in May, we were at a Microsoft CEO summit.

So many of us listened to your really description about the amazing impacts of AI on the society.

I thought that what you said really resonated with me.

And to some extent, even shocked me.

So you kind of took us back to the entire history of a human being.

You said, well, there's this agricultural revolution, so we actually manufacture more food.

Then we have industrial revolution, we actually manufacture more iron steels.

Then we have information technology, we actually have more information.

Now, in this intelligence, this era, so what you are doing at NVIDIA, what AI is doing is actually manufacturing intelligence.

Can you elaborate a little bit more on why this thing is just so enormously important?

Yeah, when you look at what we've done together, Harry, and you were in the middle of all of this, from the perspective of computer science, we've reinvented the whole stack.

Meaning, the way we used to develop software, and when you think about computer science, you have to think about software development, how software is done.

And so we used to code software with our hands.

We imagined what the function it is that we would like to implement, whatever algorithm we'd like to implement, and we use our own creativity, we type it into the computer.

I started with Fortran, and I learned Pascal, and then C and C++, and of course, each one of these languages allow us to express our thoughts into code.

And that code runs great on CPUs.

All of a sudden, now we use observed data, and we give this observed data to a computer, and we say, what is the function that you see inside this code?

What are the patterns and relationships that you observe by studying all of the data that we presented to you?

And instead of using code, coding, we now use machine learning.

And the machine generates not software, it generates neural networks that are processed on GPUs.

And so from coding to machine learning, from CPUs to GPUs, and because GPUs are so much more powerful, the type of software we can now develop is extraordinary.

And what sits on top of it is artificial intelligence.

That's what emerged.

And so computer science has really been transformed pretty much at the description I just said.

Now the question is, what happens to our industry?

Of course, we're all racing to use machine learning to go discover new AIs.

And what is AI?

And maybe that's, of course, one of the things about AI that you know very well is the automation of cognition.

Automation of problem solving.

And problem solving could be distilled down to, if I could, three basic ideas that you observe and perceive the environment, understand it, reason about it, and then come up with a plan to interact with it, whatever you decide your goals are.

And so perception, reasoning, and planning.

The three fundamental steps of problem solving.

Well, perception, reasoning, and planning could be broken down into, for example, perceiving the environment around your car, reasoning about the location that you are and the location of all the other cars around you, planning how to drive.

So I just described self-driving cars.

That self-driving car, in one manifestation, would be called a digital chauffeur.

And then you could do the same thing with you observe a CT scan, you understand it, you reason about everything that you see and you come to the conclusion there might be some anomaly that might be a tumor or something, and then you might decide to highlight it and describe it to the radiologist.

Now you're a digital radiologist.

In almost everything that we do, you can come up with some expression that artificial intelligence could then perform a particular task.

Well, what happens is if we have enough of those digital agents, and those digital agents are interacting with the computer that's generating these digital artificial intelligence, digital intelligence, the total consumption of all of us into a data center makes the data center look like it's producing this thing called tokens or what we call tokens, but otherwise digital intelligence.

And so now let me now describe it a little bit differently. 300 years ago, as you know, General Electric and Westinghouse came up with a new type of instrument.

In the beginning, a new type of machine that was called a dynamo and eventually became an AC generator.

And they were so smart to go and invent a consumer, a consumption of the electricity that they were able to produce.

And that consumption, of course, would be things like light bulbs and toasters, right?

They created all kinds of digital appliances or electrical appliances that consumes the electricity that these plants would produce.

Well, look at what we're doing now.

We're creating co-pilots and chat GPTs. We're creating all these different intelligence, basically light bulbs and toasters, and think of them as, right?

There are appliances that all of us would use, but you would connect it to a factory.

It used to be an AC power generation factory, but this new factory is digital intelligence factory.

And so what is just, from an industrial perspective, really what's happening here is we're now creating a new industry, and this new industry takes energy in and produces digital intelligence out.

And that digital intelligence would be used by all kinds of different applications.

And the consumption of it, we believe, is gonna be quite large.

And this entire industry never existed before, just like the AC generation industry never existed before that.

But that's really, truly amazing.

Jensen, you are describing this really bright future for us.

Of course, we know this thing is going to happen.

Really much of that's because of your efforts and Nvidia's contribution to the field, especially over the last 10 years, 12 years.

So one number just keeps coming back and people are talking about, in the name of scaling law and others, most recently, in your name, there's something called the Huang's Law, in comparison with to Moore's Law.

Of course, in the earlier, in computing industry, Intel came up with Moore's Law, basically meaning every 18 months, computing power will increase, will double.

And then now, if we look at last 10, 12 years, under your leadership, it's not even, every year double, it's more than that.

If we look at it from the consumption side, all those large language models over the last 12 years, every year, it's actually more than four times an increase of the computing needs.

Now every year, it's four times.

Then in 10 years, it's an enormous number, it's actually a million.

So that's how, at least I explain to people why Jensen's stock went up 300x in 10 years.

If you think about it, the computing needs is a million times more, so that's, then it explains the stock probably is not that expensive.

My question for you then is that, as you look at, as you look into the future with your crystal ball, are we going to see that a million times more needs increase for the next 10 years?

So, Moore's Law depended on two concepts.

One concept was VLSI scaling, and that was because of Carver Mead.

And the text by Mead and Conway really inspired my generation.

The second is Dennard scaling.

Constant current density scaling of transistors coupled with the shrinking of the transistors made it possible for us to double the performance, if you will, double the performance of semiconductors every couple of years.

And so every one and a half years, so that would be 10 times every five years, 100 times every 10 years.

And the other, what we're experiencing now is that the larger your neural network can become, and the more data that you train that neural network with, the more intelligent the AI seems to become.

It's an empirical law, just like Moore's Law was.

We call that the scaling law, and the scaling law appears to be continuing.

But the one thing that we also know about intelligence is that pre-training, just taking all of the data in the world and discovering knowledge from it automatically, pre-training is not enough.

Just as going to college and graduating from college is a very important milestone, but it's not enough.

There's post-training, which is learning a particular skill very deeply.

And post-training requires reinforcement learning, human feedback, reinforcement learning, AI feedback, synthetic data generation, multi-path learning, reinforcement learning.

There's a whole bunch of techniques, but basically, you're now going deep into a particular domain, and you're trying to learn something very, very deep about it.

That's post-training.

Once you select a particular career, you're gonna do tons and tons of learning again.

And then after that, of course, it's called thinking.

And that's what we call test time scaling.

Some things, you just know the answer to.

Some things, you have to break the problem down into step-by-step-by-step, into its first principled elements, and from first principles, try to find a solution for each one of them.

It might require you to iterate.

It might require you to simulate various outcomes because the answer is not predictive, and so on and so forth.

And so we call that thinking.

And the longer you think, maybe the higher quality the answer would become.

So notice, we now have three areas of artificial intelligence development where a great deal of computation would result in higher quality answers.

Today, the answers that we have are the best that we can provide.

But we need to get to a point where the answer that you get is not the best that we can provide, and somewhat, you still have to decide whether is this hallucinated or not hallucinated?

Does this make sense?

Is it sensible or not sensible?

We have to get to a point where the answer that you get, you largely trust.

You largely trust.

And so I think that we're several years away from being able to do that, and in the meantime, we have to keep increasing our computation.

Now, one of the things that you said earlier that I really appreciate is that in the last 10 years, we increased the performance by a million times.

What have we really done?

What NVIDIA has contributed is that we've taken the marginal cost of computing and we've reduced it by a million times.

Imagine if there's something in the world that you rely on.

It could be electricity.

It could be airline ticket.