Nvidia Corp Goldman Sachs Communacopia + Technology Conference 2026

Thank you. Great to be here.

We're doing that. Just take a pause.

The question is, why did I know that? The question is, why did I know that? It is actually fairly simple. The last industrial revolution made it possible for us to power everything, and we distributed power everywhere. Then, of course, the internet made it possible so that we can find anything. That is the big idea. You plug into the wall back in the old days, Ethernet jack or a modem, and now Wi-Fi. You plug it in, and you can find anything. It is not a small thing. It is a very big deal. Because you could find anything, and because everybody wants to find everything, we had to make it part of the infrastructure. Because we can distribute power anywhere, we want to power everything, we distributed power everywhere. It became part of infrastructure.

Now we can find anything, find everything, but obviously that is not what people want. What we all want is to know everything. We want to ask anything, know everything. That is the layer of computing that we are building now that makes it possible so that whatever you want to ask and whatever you want to know, you can. That is the big idea with artificial intelligence, and a computer is in the middle of doing that. The first instrument was called a dynamo, and the next one, obviously, called the computer, and now we have these AI factories, and they produce numbers just like the internet produces numbers. At some level is that simplistic. Now, the question is very simple. How did the computer industry go from that to this, and what is the implication? There are two compounding problems, two compounding challenges.

The first one is that in the last generation of computers, it was used by humans, and because you are looking things up, and you bought the computers as a tool, as a terminal, and so that you could find anything, find everything. Now, in this new world, as long as you prescribe to the idea that everybody wants to ask anything, find and know everything, if that is a world that you prescribe to and that you believe in and that it could somehow be integrated into almost everything we do, then the question is, how do you build that layer of computing around the world? That computer, as it turns out, is a generative computer. It is not a retrieval computer, and I think I have explained this to you guys in the past. The last 60 years, everything was pre-recorded, and we put it on storage somewhere.

When you touch something on the phone, it goes and retrieves that piece of information, and in fact, it probably retrieved somewhere between 3 to 10 pieces of information. Based on who you are, the cookies that are associated with you, and your previous track record and your previous preferences, a recommender system would recommend one of those pieces of information to you, but it was all pre-recorded. That model of computing is called retrieval-based. If your question—if you want to ask anything and know everything, you want to actually know it, you do not want to find it, you want to know it, then you have to generate the answer. You cannot reasonably retrieve that. The reason for that is because in the old days, you have this idea called a recommender system, and it is based on your preferences.

Well, if you want to ask anything and know everything, then that preference has to be replaced by something else, and it is called context. The query is called a prompt, and the surrounding environment includes the context and also your preferences. The combination of all of that, you have to generate the answer. Basically what that says is that a new layer of computers that is continuously generating answers based on all the queries that are coming at it has to get built. That is no longer a bunch of storage. It is a bunch of computers. What does that computer look like, and what is the algorithm it runs? It is almost like everybody has our own recommender engine. Think of it that way. Instead of having one giant RecSys engine for all of Meta, you now have a recommender engine literally for everybody.

These recommendation engines are really complex because the AI models are large. It has to be smart. It compresses a lot of the world's information. Now the second problem goes like this. It is the end of Moore's law. You guys, the first time I said it some 15 years ago, there was a gasp, like I said something that hurt somebody's feelings. It just says transistors do not scale anymore, and it is not a big deal. If the transistors do not scale 2x per year or get half as big every other year, then the question is what is going to happen to the future as we are trying to do this other thing? It is called generative AI or artificial intelligence as you might like to think about it.

In this new world, where it is the end of Moore's law and we need a lot more transistors, then several things has to happen. The first thing that has to happen is you still want to get 100X and 1,000X improvement every few years, and if you want to do that, then you cannot just live inside the chip. You have to co-design, which is the reason why NVIDIA became a co-design company, extreme co-design company. You guys hear that all the time from us. The second thing that you have to do is if you want twice as many transistors, you got to build twice as many chips. Which is the reason why NVIDIA invented NVLink. You see, first thing that we did was we used CoWoS, and that made it possible for us to fuse multiple chips together.