Ep17. Welcome Jensen Huang | BG2 w/ Bill Gurley & Brad Gerstner

**Jensen Huang** (0:00)
What they achieved is singular. Never been done before. Just to put in perspective, 100,000 GPUs, that's easily the fastest supercomputer on the planet. That's one cluster. A supercomputer that you would build would take normally three years to plan, and then they deliver the equipment, and it takes one year to get it all working. We're talking about 19 days.

**Brad Gerstner** (0:41)
Jensen, nice glasses.

**Jensen Huang** (0:43)
Hey, yeah, you too.

**Brad Gerstner** (0:45)
It's great to be with you.

**Jensen Huang** (0:46)
Yeah, I got my ugly glasses on, just like you.

**Brad Gerstner** (0:48)
Come on, those aren't ugly. These are pretty good. Do you like the red ones better?

**Jensen Huang** (0:52)
There's something only your family could love.

**Brad Gerstner** (0:55)
Well, it's Friday, October 4th. We're at the Nvidia headquarters just down the street from Altimeter. Welcome. Thank you, thank you. And we have our investor meeting, our annual investor meeting on Monday, where we're gonna debate all the consequences of AI, how fast we're scaling intelligence. And I couldn't think of anybody better, really, to kick it off with than you. I appreciate that. As both a shareholder, as a thought partner, kicking ideas back and forth, you really make us smarter. And we're just grateful for the friendship. So thanks for being here.

**Clark Tang** (1:25)
Happy to be here.

**Brad Gerstner** (1:26)
You know, this year, the theme is scaling intelligence to AGI. And it's pretty mind boggling that when we did this two years ago, we did it on the age of AI, and that was two months before ChatGPT. And to think about all of its change. So I thought we would kick it off with a thought experiment, and maybe a prediction. If I colloquially think of AGI as that personal assistant in my pocket.
If I think of AGI as that colloquial assistant in my pocket.

**Jensen Huang** (1:54)
I was getting used to it.

**Brad Gerstner** (1:54)
Exactly. You know, that knows everything about me. That has perfect memory of me, that can communicate with me, that can book a hotel for me, or maybe book a doctor's appointment for me. When you look at the rate of change in the world today, when do you think we're going to have that personal assistant in our pocket?

**Jensen Huang** (2:16)
Soon, in some form. Yeah, soon, in some form.
And that assistant will get better over time. That's the beauty of technology as we know it. And so I think in the beginning, it'll be quite useful, but not perfect. And then it gets more and more perfect over time, like all technology.

**Brad Gerstner** (2:36)
When we look at the rate of change, I think Elon has said the only thing that really matters is rate of change.
It sure feels to us like the rate of change has accelerated dramatically, is the fastest rate of change we've ever seen on these questions because we've been around the rim like you on AI for a decade now. You even longer. Is this the fastest rate of change you've seen in your career?

**Jensen Huang** (3:01)
It is because we've reinvented computing.
A lot of this is happening because we drove the marginal cost of computing down by 100,000X over the course of 10 years. Moore's law would have been about 100X. And we did it in several ways. We did it by, one, introducing accelerated computing, taking what is work that is not very effective on CPUs and put it on top of GPUs. We did it by inventing new numerical precisions. We did it by new architectures, inventing a Tensor Core. The way systems are formulated, MVLink added insanely fast memories, HBM, and scaling things up with MVLink and InfiniBand, and working across the entire stack. Basically, everything that I described about how Nvidia does things led to a super Moore's Law rate of innovation. Now, the thing that's really amazing is that, as a result of that, we went from human programming to machine learning. And the amazing thing about machine learning is that machine learning can learn pretty fast, as it turns out. And so as we reformulated the way we distribute computing, we did a lot of parallelism of all kinds, right? Tensor parallelism, pipeline parallelism, parallelism of all kinds. And we became good at inventing new algorithms on top of that, and new training methods, and all of this invention is compounding on top of each other as a result, right? And back in the old days, if you look at the way Moore's Law was working, the software was static.
It was precompiled as shrinkwrapped, put into a store. It was static. And the hardware underneath was growing at Moore's Law rate. Now we've got the whole stack growing, right? Innovating across the whole stack. And so I think that that's the... Now all of a sudden, we're seeing scaling, right? That is extraordinary, of course. But we used to talk about pre-trained models and scaling at that level, and how we're doubling the model size and doubling, therefore appropriately, and doubling the data size. And as a result, the computing capacity necessary is increasing by a factor of four of a year. That was a big deal. But now we're seeing scaling with post-training, and we're seeing scaling at inference. Isn't that right? And so people used to think that pre-training was hard, and inference was easy. Now everything is hard, which is kind of sensible. The idea that all of human thinking is one shot is kind of ridiculous. And so there must be a concept of fast thinking and slow thinking, and reasoning and reflection and iteration and simulation and all that. And that now it's coming in.