Transcription
Welcome to GTC Taiwan. So great to see all of you. Very good to be home. I brought my parents home. Where are my parents? Everybody give a round of applause to my mom and dad. >> [applause] [applause] >> And a round of applause for our pre-game show superstars. Ladies and gentlemen, >> [applause] >> look how adorable they are. The superstars of Taiwan. Uh, there are so many of you here today. We are broadcasting this right now to 70 other watch parties across Taiwan. 70 different conferences are going at the same time. Everybody is watching this keynote. We have so much to tell you, and I have so many partners to thank. It is incredible how large our ecosystem in Taiwan has become.
Most of the time when people think about ecosystem, they think about our software stack. They think about the developer ecosystem above the computing systems that Nvidia builds. But Nvidia's ecosystem spans all the way upstream to all of our supply chain here in Taiwan, where it all begins, and downstream all the way to data centers and eventually to end users. Today, we're going to talk about almost all of the ecosystem. There are so many people to thank. I love my ecosystem here. I mean, there are so many companies here, and some of my favorite ecosystem partners. >> [applause] >> So many Taiwan's rich ecosystem. The richest ecosystem, the world's best supply chain ecosystem. Unbelievable. Well, thank you all for being here. And this year, this year our businesses together are growing incredibly. In fact, somebody told me last night that the annual GDP of Taiwan is going to grow almost 10%. >> [applause] >> Unbelievable. Well, we have a lot to talk about. Let's get going.
Two years ago when I was here, I started to talk to you about how AI has moved from generative AI and the other waves of AI that are coming. The next wave of AI was agentic AI. And today, we can say that agentic AI has arrived, that useful AI has arrived. Now, what does this mean? This is GitHub. This is, of course, one of the first applications of agentic AI is software coding. One of the most valuable professions. Incredibly large ecosystem. 30 million, 40 million professional software developers, probably another couple of hundred who are students and enthusiasts and so on and so forth. But say 30, 40 million software developers in the world code for a living. And this represents most of them. This is GitHub. The pull request is when they download software, they modify it, and commit is when they push it back up. Okay? And so, if you could look at this, in 2023, the number of commits was 300 million. 2024, 400 million. 2025, 500 million commits in the first few months. In the first few months of 2026, it has nearly tripled.
Now, what does that mean? 30 million software developers representing about 3 trillion dollars worth of GDP producing three. That's what they're paid. 3 trillion dollars worth of salaries per year, which is generating economic growth for the rest of the industries. Say 100 trillion dollars of the world's industries is impacted is generated by 3 billion dollars worth of salary. That 3 trillion dollars, excuse me, 3 trillion. That 3 trillion dollars worth of salary is now producing nearly three times as much output. It's effectively a 9 trillion dollar productivity from 3 trillion dollars of salaries. Does that make any sense? The difference is absolutely extraordinary. This is the potential. This is the promise of AI. The number of engineers, software engineers, is actually increasing. People talk about AI reducing jobs. Complete nonsense. It's causing more software engineers to be hired, and the reason for that is very simple. If you can hire a software engineer, and you could generate $9 worth of productive work, why wouldn't you want to hire more software engineers? If that line was flat, then obviously people will hire fewer software engineers. But because the output is so incredible, people want to hire more software engineers. This is going to show up in our economy somehow soon. And so, the first thing is useful AI has arrived.
Now, what does that mean from the industry's perspective? From the industry's perspective, that means that tokens are now in extraordinary demand. Because if you could do this, you're going to want to produce more of it. And because tokens are now profitable units, tokens are now profitable units of revenues. Because it is now profitable, the AI companies want to build a lot more tokens, generate a lot more tokens, build more AI factories, which is the reason why compute demand here in Taiwan has skyrocketed. It is precisely the reason why all of you are so busy, and your businesses are doing so well. In fact, that looks like some of your stock price. >> [applause] >> The compute pattern has changed. Everything has changed. So, the first idea is that useful AI has arrived. AI is now a profit generator. AI is now a GDP generator. And behind it is a whole new kind of computing pattern. Not just a large language model, but an agent. Today, almost everything we're going to talk about is going to be based on this.
So, let me take a quick moment and show you what I'm talking about. Inside in this is a, this is an agent. It's an agent application. In the old days, this would be application. This would be code. And this would be operating system. Application. Code running inside an application inside an operating system. Today, it is agent which consists of a large language model or many sitting inside a harness. And that harness helps it orchestrates it to do productive work. This is the input. When that input comes, it has to understand, observe, reason, act, use tools. Use tools. That tool could be a spreadsheet, web browser, a data processing engine, database engine, for example. This is orchestrated. This harness orchestrates this routing of information every single time it touches either processing the context, understanding what is happening, reasoning about what to do, coming up with a plan that you can act that it acts on. That orchestration path is orchestrated by some software. And so, this is fundamentally an agent. It deals with short-term memory, called working memory, long-term memory, just like we do. We have long-term memory. And so, the memory management system is incredibly important. This entire system is called an agent. The large language model is used to do the thinking. And the harness connects everything together, just like an operating system. Okay? And so, this is the new computing model, and this is what an agent. It could do incredible things. This is the big breakthrough. The simultaneous convergence of large language models that are now able to do a really good job thinking, reasoning, planning, using tools, and the fact that we have now these harnesses that manages memory, the orchestration, uses tools, we can now do amazing things. >> [music]