【原音呈現LIVE】安謀執行長Rene Haas 發表主題演講

There's a specific kind of silence right before the world changes. It's that split second of realization. The moment when you know that life as you've lived it is about to become something entirely new.

For more than 35 years, we've learned to recognize that moment. From Cambridge to the US to Taiwan to the world, we partnered with our ecosystem, solving problems together. We've been here from the start of AI, preparing the world for what's next. Empowering intelligence everywhere in how we live, work, play, and move together. This is our moment. Because when you know you have the power to change everything, you step forward.

That's a pretty cool video of Rene. Superstar.

Not just a superstar, he's an action hero. I think the night market was the part I thought was the coolest.

Yeah.

Yeah.

Thank you.

Look at his stock price. I announce a product. Look at his stock price. Every product I announce, his stock price goes up.

Let's also stay for the record.

Let's also stay for the record that you were a shareholder and you sold.

So, how... what were we talking about? RTX Spark.

Well, we wanted to reinvent the computer. You know, the PC has been here for 40 years and the operating system code written by hand is now going to be replaced with applications that are agentic. Now, these agentic systems, agentic AIs will use the PC, will use the tools in the PC. And so when we imagined this future we thought let's see how would we change the architecture and how would we change the operating system and reinvent the computer and you know that's kind of where we are. And so one of the things that we realized is that an agentic system really wants to have excellent CPUs which is the reason why we used ARM and it has a 20-core CPU, has to have excellent single-threaded performance, the parameters, the memory has to hold a lot of parameters. And so we created a new numerical format called NVFP4 so that we can compress the large language models as much as possible and fit a very smart AI into the system memory. We also wanted to unite CUDA that is for accelerated computing and CUDA cores, our tensor core processing into one processor. And the reason for that is because when you're operating these agents and they're thinking and they're using the tools, the agents are fast. And when the agents are fast, they expect the tools to respond quickly. And so that's why we're accelerating all of the tools. We're accelerating Adobe. Adobe announced they're going to rearchitect Adobe Photoshop and Premiere so that it's CUDA accelerated and agentic-ally accessible. And so we're accelerating applications. We accelerated Blender with RTX. We accelerated, you know, we're going to accelerate everything. We accelerate Adobe, Autodesk, Siemens, we're going to accelerate every tool. And once these tools are accelerated then they can respond to the agents very quickly. And so we now in order to build this computer, this SoC, unless you have the ability to integrate with the CPU and adapt the CPU to exactly the shape of the computer, it's really quite impossible which is the reason why ARM is perfect.

And the key word there is ARM is perfect.

Well, you know, when ultimately these computers, these personal computers are going to be becoming agents that are running all the time. They're autonomously running all the time. I could imagine I today if I left my laptop at home where I left my laptop in the hotel, I won't use it again until I get there. But in the future, you just pick up your phone and you chat with your agent. You're chatting with your PC in the future and that you maybe there's something that you needed to have done and sent to you. Maybe there's a speech I need to have quickly written. And so, you know, I'll be working with my agent, working with my assistant, and that is now the ARM personal computer, right? And so the PC is working in the back while you're not there. It's working. Yeah. And so, if I want to do something that requires a cloud API, of course, I'll call it into the cloud API. But whatever I can do locally, we're going to continue to do on the PC, which is kind of the nature of PC.

Well, if you look at the RTX Spark PC, it's got 128 gigabytes of memory. If it was completely compressed into NVFP4, then you can have a 100 billion parameter model working on your PC all the time. And a 100 billion parameter open model, say Nemotron 3 Super, say that's a really, really good model. Yeah. And so it could do a lot of the basic work and whatever deep thinking and frontier model that you need to use, it's just connected to cloud anyhow. And so it allows you to have a really great personal computing experience, you know, your own experience, but whatever you need to connect to the cloud, you will.

These agents are going to be, you have agents and sub-agents and teams of agents. They're going to be working in the cloud. They're going to be working on devices. And so it's just like today in a lot of ways. Mobile-cloud is not cloud-only, not mobile-only. It's mobile and cloud.

Well, the operating systems get to be just as important as ever before, if not more important. And the reason for that, and this is the controversial part that people say AI comes along, software is dead. You know, nothing is further from the truth. And now people are starting to realize that when agents are here, they're going to use tools. And so those tools are more important than ever. And so they're going to use Adobe Photoshop, they're going to use Adobe Premiere, they're going to use Canva, they're going to use the, you know, they're going to use the tools, Siemens tools, they're going to use, you know, tools, whatever they have on the device. They're just, this is the incredible part today. Most of us probably know 10, 15, 20% of the features of a tool. If you know how to use Photoshop, you know, use Lightroom, unless you're an expert like my son, it's kind of hard for you to know all of the features. But now with your agent, you tell the agent what you're looking for and the agents know exactly how to use the tools because it's read a skills file. It's essentially read the manual of that tool. And so now it goes and uses the MCP or the CLI connected to that tool and it does everything you needed to do. Yeah. So, it's going to unlock all these tools. These tools are going to be more useful and more valuable than ever. And that these tools run on the operating system. So, we're going to need Windows. We're going to need, you know, all these APIs and all these tools for a long time.

Well, it's probably going to be everywhere. This is, at this point if you look at our evolution, first Hopper was designed for training. Then Grace Blackwell was of course great at training, but we also specialized NVLink 72 for inference. And at first people thought, you know, inference was easy. And we explained to people that large language models and to be able to inference very quickly and generate these tokens as efficiently as possible, you're going to need a very complicated computer. And so Grace Blackwell NVLink 72 is the most efficient and we produce the lowest cost tokens in the world. Okay. And so that was a big breakthrough and now people understand that you want very advanced systems to generate tokens at very low cost. Vera Rubin took of course all of that and we extended it to run agents. At first when I said that two years ago most people had a hard time understanding what that meant but now they realize that an agent is orchestrating, thinking, is using tools. It's accessing long-term memory. It's dealing with short-term memory, working memory and it's doing memory compaction to remember, to think about what should I remember for the future, how do I index SQL memory, how do I index structured memory, how do I index unstructured memory. And so how do I deal with all of that. That agentic system is what Vera Rubin is and it's a large system and so people are now starting to understand that when we were thinking about agentic systems we were really thinking about new computing application pattern and that it really requires a new architecture. Well, now the big breakthrough of course these agents now are producing useful AI and that's the reason why all of our growth, your growth, my growth is just so incredible because when AI becomes useful then the tokens that are being generated are profitable and when token generation is profitable everybody wants to generate a trillion times more tokens. The other part is that an agent, the application, this agent compute pattern is a thousand times, maybe a hundred thousand times and depending on the work it's a million times more than chatting. Yeah. And so you could see that the agents are working, they're working for, you know, minutes, hours, sometimes days, sometimes weeks. And so instead of a chatbot which responds from one click, now the AI is thinking, using tools, reading, thinking some more, planning, trying. And so the amount of tokens that we have to generate has increased tremendously. The profitability of the tokens obviously is driving demand. So the compound effect of need more compute with more demand, that compounded effect is what you and I are experiencing. And so we're seeing constraints almost everywhere. In our case, you know, we were fortunate that we planned, you know, one of the best things about ARM is that they don't have to worry about the supply chain. You know, the supply chain of IP is electrons and you could use as many electrons as you need. Okay. And so I love his business model. I mean, as you know, I tried to buy it. I tried to become ARM. We were willing. Rene and I used to work together and then we tried to work together again. But anyways that was okay. I'm sad still. I'm a little sad but this is a happy meeting. So my point is in our case we saw agents coming and we saw Vera Rubin coming. So we did a good job planning our supply chain and so our supply chain can support our very robust growth. We grew almost 100% year-over-year this year. We're going to grow very aggressively next year and so our supply chain could support our growth. But the fact of the matter is demand is even higher than that.

That's right. Take a step back. What's happening? Take a step back and think what's happening. What's happening is the computer industry was limited by the number of people using the computers. And now we have agents that are autonomously using computers. And so we're going to have instead of 1 billion humans using computers, we will have tens of billions, maybe more than that of agents and robots and self-driving cars using computers. And so the question is how large can the computer industry be? Yeah. And so, you know, my sense is that at this point it's a foregone conclusion that what is a trillion dollar, multi-trillion dollar industry is likely 10 times larger. Yeah. And so, we're on our way to... and that's why Nvidia is the, you know, the largest market cap company in the world. And if you combine the two companies, we'd be the largest in the world still.

Really?

I have to tell you, I think I aged better.

I feel like I aged pretty well.

If I sign it back to you, it'd be treasure.

We can't do that. We know that game. All right.

Buy ARM. Buy ARM. I tried.