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 worth of GDP producing, that's what they're paid, $3 trillion worth of salaries per year, which is generating economic growth for the rest of the industries. Say a hundred trillion dollars of the world's industries is impacted, is generated by $3 trillion worth of salary. That $3 trillion worth of salary is now producing nearly three times as much output. It's effectively a $9 trillion productivity from $3 trillion in 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 trillion worth of productive work, why wouldn't you want to hire more software engineers? If that line was flat, then obviously people will hire few more 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.
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. 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. 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. It's hardest to orchestrate 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 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 is. 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. Let me give you some example. This is a prompt. This is the code that is generated. And this comes out. This is the input. This is the input and that's the output. Do you guys, what do you guys think? It's pretty amazing, right? We use Claude Code here, but Codex does an incredible job as well. Here's another example. This is the input. Create a GIF. NVIDIA green dots on black scatter form Taiwan 101 building. Morph to GTC Taipei 2026. Morph to NVIDIA logo then scatter and repeat. Right? So you saw that. That was the prompt. Here's the next one. I lost my remote control battery clip. It looks like this. Create a CAD file. It uses a tool. Create a CAD file ready for 3D printing to create a new one. Make sense? This is now the new computing pattern. Whereas we used to launch an application, click and type, we now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools and produce the necessary output. This is how computers are going to work in the future. This is agentic AI. For two years, we've been building towards this and now it has arrived. Now, one of the big breakthroughs, of course, is tool use. A lot of people have said, you know, Jensen, AI is coming. Agentic AI is coming. Therefore, all of the software companies are going to go out of business. I said, it's exactly the opposite because there are going to be so many agents. The world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what NVIDIA's treasure is, is all of our CUDA libraries. I call them CUDA-X libraries. This is NVIDIA's treasure. Today, we're able to now present these CUDA-X libraries to agents who can use it much more effectively than even humans. And so this is a wonderful time for CUDA-X libraries. Let's take a look.
20 years ago, we built CUDA, a single architecture for accelerated computing. We reinvented computing. A thousand CUDA-X libraries help developers make breakthroughs in every field of science and engineering. CUDA-X libraries are tools for agents. cuLitho for computational lithography. cuOpt for decision optimization. cuDSS for direct sparse solvers. AIQ for deep research across structured and unstructured documents. Aerial for AI-RAN. Warp for differentiable physics. Parabricks for genomics. At their foundation are algorithms and they are beautiful.
A round of applause for math. Math is beautiful. The computing pattern, the computing pattern of software is going to change. In fact, let's come back to this. This is the agent. It is the ultimate disaggregated and distributed computing model. So many different computers are going to be activated in order to process this agent. The agent consists of model, harness, tools and skills, and a runtime. All of that is running at different places in a data center. You can think of the model as the brain, the harness as the body, the tools that it uses working in a runtime. Think of it as a workshop. So this is a person, a worker working with tools in a workshop. Of course, this is being done at extraordinarily large scales and each one of those steps are running in a different part of the computer. And you could see the large language model is thinking, context processing, observing, understanding the environment, reasoning, coming up with a plan, and acting on the plan. Every single time that happens, an entire rack of Grace Blackwell NVLink 72 is activated. It's thinking with a large language model. Whenever it uses a tool, a CPU is used. That tool could be a C compiler, it could be Python, it could be JavaScript, or it could be accelerated computing. Today's agents are relatively simple users of tools. Tomorrow they're going to be very sophisticated users of tools, which is the reason why the CUDA-X libraries that I showed you are going to be incredibly popular with agents. They solve some of the most important problems the world knows. And all of our CUDA-X libraries are now going to come with skills that the AI could learn how to use. So the CUDA-X library, some skills, basically a manual, the AI reads it and go, aha, that's how you use it. The ability to use these libraries by agents are going to be incredible. And so the tools run on CPUs and GPUs and large language models. The security harness runs on CPUs and a security processor called a DPU, NVIDIA's BlueField. The orchestration of all this runs on a CPU. This is the entire harness and the CPU is orchestrating all of the work. One of the hardest parts is memory. You could just imagine the working memory is called KV caching. What to remember? Compaction, not just compression, but how to retrieve. Do you retrieve structured data? Do you retrieve unstructured data? What is the ontology, the relationship of all of these different data to itself? That entire processing is incredibly complicated. The memory system, the memory system of AI is going to cause the storage system to be completely revolutionized. As you could see, every aspect of this computing model, this computing pattern, this new application called an agent is fundamentally different than the way that applications used to run. A whole bunch of software sitting inside a binary sitting inside an operating system. This is the reason this disaggregated, this distributed, this heterogeneous computing problem is precisely the reason we built our next generation. Vera Rubin. Vera Rubin is not one chip. Vera Rubin is not a GPU only. It starts with the GPU. But Vera Rubin is incredible. This entire thing is Vera Rubin from end to end. It has GPUs, Vera Rubin NVLink 72. It is orchestrated by Vera CPUs that I'm going to tell you more about, the storage systems, revolutionary Vera, along with CX-9, our software stack called DOCA, the security processor that's inside so that everything is encrypted at rest, in motion as well as in use. Everything across this is secure because the AI model is so precious. This is the reason why this entire system obeys confidential computing. Each one of these systems would be a complete revolution in itself. Vera Rubin is the most ambitious endeavor in the history of our company. The whole company worked on Vera Rubin, across all 40,000 engineers. Not to mention all of you, all of you participated in the creation of this entire system. Vera Rubin is really a miracle. And it's not just one chip. It is so many. Well, it's even beyond that. A long time ago, NVIDIA used to be a GPU company, but over the years, we've evolved to become a systems company. You're looking here now for the most complex system, most complex and ground-up system ever designed. But ultimately, our customers, our partners don't want to buy computers. They want to build AI factories. Which is the reason why NVIDIA has really started to transform ourselves yet again. You could see so much of our technology is now at the entire infrastructure scale. Our partners are at infrastructure scale. Power generators, cooling systems, the grid providers, so many industrial companies are now part of our ecosystem because ultimately we're trying to build an entire stack just like GPUs, just like when we were building Grace Blackwell, NVLink 72, just like now we are building a full stack system so that our customers could build amazing AI infrastructure. Let's take a look.
The world is racing to build AI factories. The largest infrastructure buildout in human history. AI factories are incredibly complex. Every layer, chip, rack, network, power, cooling, grid must be designed together from end to end because compute is revenues. NVIDIA DGX is the blueprint, a reference design for building and operating AI factories at maximum efficiency and profitability. It starts with DGX Omniverse. With the DGX Omniverse blueprint, partners design and validate an NVIDIA Vera Rubin AI factory before a single rack lands. They plan the layout, simulate the power and cooling, design the network, validate every integration, test every change in the digital twin. The factory powers on. DGX OS takes over and provisions, operates, monitors, and remediates the infrastructure, turning the installed systems into trusted, multi-tenant, resilient, AI-ready capacity. Today's AI factories overprovision power by up to 40%. DGX Max Power lets operators safely deploy more GPUs inside the same power budget, adding billions in annual revenue. Breakthrough hot liquid cooling at 45 degrees Celsius uses less water and energy. More power going to revenue-generating compute. Incredible. Dynamic power allocation steers power from rack to rack, recovering stranded watts, sending them where work is happening. In raw power smoothing flattens peak current spikes and power surges throughout the factory. Teams of AI agents work with DGX Max Power, continuously coordinating to balance cooling and power to meet workload demand. DGX AI factories are flexible energy assets that operate cooperatively with the grid. DGX Flex reads real-time grid signals and dynamically adjusts factory power when the grid needs relief. 100 gigawatts of AI factories will come online before the end of the decade. NVIDIA DGX AI factories run at highest efficiency, produce the lowest cost tokens, and make the grid stronger.
I've shown you ecosystem slides of the past where NVIDIA's computing layers and software and computing stacks are integrated into other people's platforms, third-party platforms and libraries that serves end markets. That was a computing ecosystem. This is an AI factory ecosystem. This is way downstream of all of you. Upstream of me is all of you and downstream of us is this ecosystem. Because NVIDIA ultimately is not just building a GPU, not just building a system. We're helping customers build these AI factories, these AI infrastructure that is so immensely complex. Each one of these at one gigawatt level started at 30, 20, 30 billion. It is at 50, 60 billion and soon it will be 80, 100 billion dollars per gigawatt. $100 billion into an AI factory. It must work the first time. It must work right away. The cost of capital is incredible. The complexity is incredible. So as you see we used to design a chip inside a computer and then we simulated a system inside a computer. Today you saw just now everything was built in Omniverse. I've been working with Omniverse with all of you for a long time. This was the dream come true so that we can build these gigantic systems as large as the world wants to build inside a digital framework, inside a digital simulator, in a digital world long before we build the first, break ground and put our money to work. So this is our ecosystem, we call it DGX. RTX for our GPU, DGX for our systems, and now DGX basically infrastructure. Because of the work that we do here across this entire stack, including our systems and software, it's the reason why we could work with small companies and enable them to be world-class AI clouds. Every one of these I'm about to show you are small companies just recently. And now CoreWeave is worth 50, 60, 70 billion dollars and growing incredibly fast. Recently we worked with Nebius and again they're growing incredibly fast. Each one of these clouds have incredible customers. Cursor the software coding company, Black Mountain Labs, Image Generation, World Labs, World Foundation model, Revolut, the leading financial services AI company, and Shopify. Here's another one. This is Nscale and their customers are British Telecom, Google. Google is using one of our AI clouds, Thinking Machines, a Frontier Labs company. Super exciting. Here's Naver Cloud in Korea, Bank of Korea, Hyundai. So many incredible companies. Here's one in India, Yotta. Incredible companies. Here's one based in Singapore building in Australia. Together AI, AI Singapore. This is one in Indonesia. Each one of these companies, each one of these companies are serving regional as well as global customers. AI is going to run everywhere. Every company will be powered by it. Every region will build it. Endosat here in Indonesia. Here in Taiwan, GMI. Here in Taiwan, GMI. It's okay to clap. So, incredible, incredible companies, incredible opportunity, but all of them need several things. Of course, they need the computing stack. This entire stack underneath. This is what made NVIDIA famous. All of our hardware and software and libraries, our connection into the world's ecosystem of third-party developers makes it possible for anyone to stand up an AI cloud. However, the AI cloud is so complex now. This is the software version. This is the computer science version, the money version, the asset version is what I showed you earlier. It's a giant factory. Having this ability alone is not enough which is the reason why NVIDIA has become an AI infrastructure company. Now doing this well and becoming incredibly good at helping customers build AI factories and deploying AI factories is incredibly important. And the reason for that is this. Compute is revenue. Now compute is profit. The absence of revenues and profit is loss. And so it's really important to realize that this is when, this is an example of an AI infrastructure coming online. It could take, it could be coming online quickly. It could take a while. Its throughput could be high. It could be low. Its resilience and reliability could be good or bad. And its lifetime of usefulness could be long or short because this represents 50, 60 going to a hundred billion dollars. This curve matters greatly which is the reason why NVIDIA is such a great partner working with us because of our fully integrated capability. We didn't just come up with a PowerPoint slide. We created the entire infrastructure. We connected everything together. We built out billions and billions of it ourselves to make sure that everything works well. As a result of that, our time to first token, our time to first inference, our time to training turned on is much faster. Second, because our throughput per watt, our tokens per watt is utterly world-class. And the reason for that is because we integrate everything. We design everything from the ground up. We simulate the entire system and we use extreme code design. Just like I showed you just now with the Vera Rubin rack, everything was designed in order to deliver on this incredible throughput. If your data center, if your factory has one gigawatt, it will not have more. One gigawatt means one gigawatt. That's all the power generation you could do. If you have one gigawatt of power, then throughput per watt is revenues because every token is profitable. Every token is revenues. This is the future. Compute is revenues. Performance per watt is your revenues. Choosing the wrong architecture. Just because the chips are cheaper doesn't translate, doesn't make sense. You need to make sure that your revenues per watt, the more you buy, the more you make. And so tokens per watt. And then lastly, very last, oh second, third is reliability. If you ever get a chance to see these data centers, there are so many moving parts, millions of cables, the ability for all of those computers to work harmoniously, reliably is extremely low. It is just extremely difficult. We have now been operating very large scale for a very long time. That experience matters. That difference mean time between interrupts extremely important. And then lastly, this is very hard. The lifetime of these systems, the lifetime of these systems, the software is changing all the time. Four years ago, which is in the time of Hopper, AI has completely changed. Six years ago, this is the time frame of Ampere, AI has completely changed. We started out talking about CNNs, here we are. Then we talked about transformers and then we talked about mixture of experts. Now we're talking about agentic systems. Every single generation, every single few months, the software industry is coming up with new technology. If your architecture is not flexible, if your ecosystem is not rich, then this curve cannot be long. You cannot predict how long your system can last. I can. NVIDIA systems is all over the world. Software developers start with NVIDIA CUDA and by definition therefore the life, the ecosystem, the useful asset is going to be much longer. The difference is essentially cost. You could think of it as revenues, but the other side of revenues is cost. If the life of the asset is long, the TCO is low. This is the difference. This is what it looks like when compute. The more you buy, the more you make.
Now, all of you are experiencing this with me. Isn't that right? All of your demand, your factories are working so hard, your people are working so hard all across Taiwan because everybody wants to make money. They realize that AI, useful AI is here. Profitable AI is here. Compute demand is incredibly high and compute demand is the constraint. And so let's go work super super hard and help the world stand up AI factories everywhere. This is why it's so important. I'm so happy here I am standing in front of you. Vera Rubin is in full production. Vera Rubin is in full production. The supply chain we created for Vera Rubin is twice as large as Grace Blackwell. Yeah, it's incredible. And what used to take two hours to assemble one Grace Blackwell rack now only takes five minutes. So not only is the capacity higher, the throughput is a lot faster and we need it all to support the demand. This ecosystem is extraordinary. Millions of square feet has been put online to support Grace Blackwell and preparing now, ramping up now Vera Rubin. I want to thank all of you. Vera Rubin is now in full production. Thank you.
Large language models generate answers. Now AI agents can do work. But processing agentic AI is a whole different kind of problem. Agents observe, reason, plan, use tools. They manage massive context, juggling working memory and long-term memory. They spin up sub-agents, specialists on demand. NVIDIA Vera Rubin is a multi-rack pod-scale system built to process Agentic AI and is now in full production. The manufacturing, automation and orchestration across the supply chain, a miracle to witness. Our journey started when we launched the first AI supercomputer, NVIDIA DGX-1. Over the next decade, we pushed every chip and system to the limit. From Pascal and the first NVLink to Grace Blackwell, the first rack-scale AI supercomputer, and now Vera Rubin, the first multi-rack pod-scale supercomputer built for the agentic age. It starts at TSMC. The seven new chips that make up Vera Rubin take shape through hundreds of processing steps. Three nanometer process, CoWoS packaging, HBM4 memory from Micron, SK Hynix, and Samsung. The Vera Rubin compute board, 6 trillion transistors with over 18,000 components on one board. Vera Rubin NVL72 does the thinking, prompt and context understanding, reasoning and planning. Next, a new modular compute streamlined with a new PCB midplane design, Superchips, ConnectX-9 SuperNICs, and BlueField-4 DPUs, all made in place with no cables for resiliency at AI factory scale. 18 compute trays, nine hot-swappable NVLink switch trays, new high-efficiency manifolds, liquid-cooled bus bars carrying over 5,000 amps, the equivalent of 20 electric cars at full acceleration. Together, 1.3 million components form this third-generation MGX rack design. Congratulations to Microsoft for their operational Vera Rubin NVL72 engineering rack. Congratulations to Dell and CoreWeave as well for standing up their Vera Rubin NVL72 engineering rack. Then the Vera CPU rack. 256 CPUs in a single liquid-cooled rack. Orchestrating the models, shuffling memory, launching tools. At Foxconn and Quanta, Grace 3 LPX takes shape. 256 Grace 3 LPUs across 16 trays, 40 petabytes per second of SRAM bandwidth for ultra-low latency. While NVL72 generates tokens at the highest throughput, Grace LPX generates them at the lowest latency. Vera BlueField-4 STX where AI keeps its memory. Storage processing accelerated by BlueField-4, connecting memory, storage, and in-silicon security. And NVIDIA Spectrum-X Ethernet, Photonics, the world's first Ethernet switch with 200 gigabit co-packaged optics, TSMC's CoWoS process, chip-scale packaging, and ultra-high-powered laser dies on indium phosphide. Vera Rubin, five connected rack-scale systems, a supercomputer for AI agents. 150 supply chain partners across Taiwan, millions of square feet of factory floor, hundreds of sites, chips, packages, systems, and data centers pushed to the limits of size, power, and scale. This is what we call extreme code design. We did this with Taiwan. Together, we reinvented computing for the age of AI. Taiwan was with us at the beginning and here today as we bring Vera Rubin to the world. Thank you, Taiwan.
Ladies and gentlemen, Vera Rubin. Vera Rubin was not just built for AI. Vera Rubin was not built just to run AI. Vera Rubin was built to run agents. This is an agentic system. Imagine the complexity, which is the reason why agents is the last computer science breakthrough. It has taken this many years for agents to realize its potential and become useful. It stands to reason that the computer that runs it is the most advanced in the world. This is Vera Rubin. Let's take a look. Can we bring out Vera Rubin, please?
And Janine, do we have the racks, the systems? It looks heavy. This is Vera Rubin. Vera Rubin NVLink 72. This is the Grace LPX. At the next GTC, I'm going to talk to you about a lot more of this today. We have so much to talk to you about. This is Vera CPU rack. 256 CPUs, all liquid cooled. Let me tell you about Vera in just a moment. This is the Vera BlueField storage processing system and also security system. And of course this is our Mellanox networking, the world's first CPO. This is Vera Rubin. Incredible technology all coming together. Now when we built Hopper, we built Hopper as you know for pre-training. Pre-training was the most important application, the most important workload we were working on at the time. Then when we worked on Grace Blackwell, everybody said, 'Jensen, you know, NVIDIA is really good at pre-training. Inference is so easy.' Do you remember that? People used to say, 'Inference is so easy. We could do that, too.' But as you know, inference equals money. And the models are so complicated. And to do it at incredibly high response time, fast interactivity and high throughput at the same time is incredibly hard. Which is the reason why we created NVLink 72. Today NVIDIA's token cost is the lowest in the world. Not by 10%, by X factors, orders of magnitude. All because we did extreme code design. All because we understood the computing model, the computing pattern of inference and we were able to create NVLink 72. Now with Vera Rubin it is beyond inference. It is now inference in an agentic system. This is Vera Rubin. No cables, no hoses, no fans. What used to take, the last time when I showed this to you, we had cables everywhere. The cables were amazing to look at, but now there's a PCB in the middle which connects both sides. What used to take two hours now takes 5 minutes. The reliability and the resilience of Vera Rubin is going to be off the charts. This is our Vera CPU tray. The most advanced CPUs that has ever been built. I'm going to show you that in just a second. And this is our storage tray. Two Vera CPUs, four CX-9. Incredible amounts of software. This is our new LPX, LPU30, the Grace system designed for very low latency inference. The throughput is delivered by Vera Rubin and extended with NVLink 72. If you want to extend that even further, you can have Grace LPUs. Here we have the Vera Rubin NVLink switch tray. This is the switches in the middle. And this is revolutionary because of Vera Rubin's, because of NVLink 72 and the NVLink switches that we created and invented. And this is our Ethernet switches for scale out. What's amazing is we introduced these two systems for Grace Blackwell. These two systems were created for Grace Blackwell and today NVIDIA is the largest networking company in the world. I'm so proud of the networking team. This is such an incredible enabler for everything that we do. I'm going to now talk to you about the next major industry we're going to be part of. Thank you, Janine.
Okay, let's talk about CPUs. Vera CPUs. CPUs built for the age of AI. All of the CPUs until now were created for people. We were the users. We were the renters. The way we use CPUs, we live in a world counted by seconds. The way we rent CPUs in the cloud, each one of them, the more CPU cores you have, the more you can rent. The economics of the old, the use case of the old CPU and the economics of the old CPU fundamentally different than agents. Agents are impatient. They don't live in a world that is in seconds. They live in a world that's in nanoseconds. When it uses a tool, it wants the response time to be as fast as possible. When it accesses a database, it has to come back as soon as possible. Every moment that the agent is waiting keeps it from going to the next step, the next step, the next step. It is vital that we make the CPUs as low latency as possible, as interactive as possible. So we created Vera CPU for the age of AI. Now inside our system is used for three different ways. The first way of course is Vera Rubin for thinking and inside the Vera Rubin rack. They're already two CPUs. As you know we are building and selling millions of Vera Rubins. We have sold millions of Grace Blackwells. NVIDIA already is one of the largest CPU makers in the world. Vera in the Vera Rubin rack are two CPUs. One for orchestrating and managing the GPUs, managing the KV cache, dealing with all of the software that runs in the rack. We also have the Grace BlueField that is used for security and isolation. The Vera compute is used for the harness, the orchestration of the AI models, tool use, accessing the database. And the data servers are right here, Vera BlueField, the fastest storage, fastest storage servers, the fastest storage system the world has ever made. And the reason why this is so vital is because agents are accessing memory, accessing memory so incredibly fast. These systems, the storage server and the CPUs are now the critical path of the most expensive part of the data center. This is the most expensive for a good reason. The economics, the economics of the AI factory is tokens and the tokens are created here. And so of course you want to manufacture and generate as many tokens as possible. This is where you put all of your economics and this has to not be in the way. And so Vera CPU has great pressure on the CPU architecture which is the reason why we built a brand new architecture from the ground up. A CPU the world has never seen before. We call it Vera. This is CPU for agents. All the CPUs of the past. Very good to be home. I brought my parents home. Where are my parents? Everyone give a round of applause to my mom and dad. And a round of applause for our pregame show superstars, ladies and gentlemen. Look how adorable they are. The superstars of Taiwan. 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 team. 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 all of the ecosystem. There's 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. 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%. 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 AIs 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 Agent 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 worth of GDP producing, that's what they're paid, $3 trillion worth of salaries per year, which is generating economic growth for the rest of the industries. Say a hundred trillion dollars in the world's industries is impacted, is generated by $3 trillion worth of salary. That $3 trillion worth of salary is now producing nearly three times as much output. It's effectively a $9 trillion productivity from $3 trillion 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 trillion worth of productive work, why wouldn't you want to hire more software engineers? If that line was flat, then obviously people will hire few more 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. 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. 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. 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. It's hardest to orchestrate 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 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 is. 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. Let me give you some example. This is a prompt. This is the code that is generated. And this comes out. This is the input. This is the input. And that's the output. Do you guys, what do you guys think? It's pretty amazing, right? We use Claude Code here, but Codex does an incredible job as well. Here's another example. This is the input. Create a GIF. NVIDIA green dots on black scatter form Taiwan 101 building. Morph to GTC Taipei 2026. Morph to NVIDIA logo then scatter and repeat. Right? So you saw that. That was the prompt. Here's the next one. I lost my remote control battery clip. It looks like this. Create a CAD file. It uses a tool. Create a CAD file ready for 3D printing to create a new one. Make sense? This is now the new computing pattern. Whereas we used to launch an application, click and type, we now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools and produce the necessary output. This is how computers are going to work in the future. This is agentic AI. For two years, we've been building towards this and now it has arrived. Now, one of the big breakthroughs, of course, is tool use. A lot of people have said, you know, Jensen, AI is coming. Agentic AI is coming. Therefore, all of the software companies are going to go out of business. I said it's exactly the opposite because there are going to be so many agents. The world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact what we have done, as you know, what NVIDIA's treasure is, is all of our CUDA libraries. I call them CUDA-X libraries. This is NVIDIA's treasure. Today we're able to now present these CUDA-X libraries to agents who can use it much more effectively than even humans. And so this is a wonderful time for CUDA-X libraries. Let's take a look.
20 years ago, we built CUDA, a single architecture for accelerated computing. We reinvented computing. A thousand CUDA-X libraries help developers make breakthroughs in every field of science and engineering. CUDA-X libraries are tools for agents. cuLitho for computational lithography. cuOpt for decision optimization. cuDSS for direct sparse solvers. AIQ for deep research across structured and unstructured documents. Aerial for AI-RAN. Warp for differentiable physics. Parabricks for genomics. At their foundation are algorithms and they are beautiful.
A round of applause for math. Math is beautiful. The computing pattern, the computing pattern of software is going to change. In fact, let's come back to this. This is the agent. It is the ultimate disaggregated and distributed computing model. So many different computers are going to be activated in order to process this agent. The agent consists of model, harness, tools and skills, and a runtime. All of that is running at different places in a data center. You can think of the model as the brain, the harness as the body, the tools that it uses working in a runtime. Think of it as a workshop. So this is a person, a worker working with tools in a workshop. Of course, this is being done at extraordinarily large scales and each one of those steps are running in a different part of the computer. And you could see the large language model is thinking, context processing, observing, understanding the environment, reasoning, coming up with a plan and acting on the plan. Every single time that happens, an entire rack of Grace Blackwell NVLink 72 is activated. It's thinking with the large language model. Whenever it uses a tool, a CPU is used. That tool could be a C compiler, it could be Python, it could be JavaScript, or it could be accelerated computing. Today's agents are relatively simple users of tools. Tomorrow, they're going to be very sophisticated users of tools, which is the reason why the CUDA-X libraries that I showed you are going to be incredibly popular with agents. They solve some of the most important problems the world knows. And all of our CUDA-X libraries are now going to come with skills that the AI could learn how to use. So the CUDA-X library, some skills, basically a manual, the AI reads it and go, aha, that's how you use it. The ability to use these libraries by agents are going to be incredible. And so the tools run on CPUs and GPUs and large language models. The security harness runs on CPUs and a security processor called the DPU, NVIDIA's BlueField. The orchestration of all this runs on a CPU. This is the entire harness and the CPU is orchestrating all of the work. One of the hardest parts is memory. You could just imagine the working memory is called KV caching. What to remember? Compaction. Not just compression, but how to retrieve. Do you retrieve structured data? Do you retrieve unstructured data? What is the ontology, the relationship of all of these different data to itself? That entire processing is incredibly complicated. The memory system, the memory system of AI is going to cause the storage system to be completely revolutionized. As you could see, every aspect of this computing model, this computing pattern, this new application called an agent is fundamentally different than the way that applications used to run. A whole bunch of software sitting inside a binary, sitting inside an operating system. This is the reason this disaggregated, this distributed, this heterogeneous computing problem is precisely the reason we built our next generation. Vera Rubin. Vera Rubin is not one chip. Vera Rubin is not a GPU only. It starts with the GPU. But Vera Rubin is
Incredible. This entire thing is Vera Rubin from end to end. It has GPUs, Vera Rubin MVLink 72. It is orchestrated by Vera CPUs that I'm going to tell you more about, the storage systems, revolutionary Vera along with CX9, our software stack called DOA, the security processor that's inside so that everything is encrypted at rest, in motion, as well as in use. Everything across this is secure because the AI model is so precious. This is the reason why this entire system obeys confidential computing. Each one of these systems would be a complete revolution in itself. Vera Rubin is the most ambitious endeavor in the history of our company. The whole company worked on Vera Rubin across all 40,000 engineers. Not to mention all of you. All of you participated in the creation of this entire system. Vera Rubin is really a miracle and it's not just one chip. It is so many.
Well, it's even beyond that. A long time ago, Nvidia used to be a GPU company. But over the years, we've evolved to become a systems company. You're looking here now for the most complex system, most complex and ground-up system ever designed. But ultimately our customers, our partners don't want to buy computers. They want to build AI factories. Which is the reason why Nvidia has really started to transform ourselves yet again. You could see so much of our technology is now at the entire infrastructure scale. Our partners are at infrastructure scale. Power generators, cooling systems, the grid providers. So many industrial companies are now part of our ecosystem because ultimately we're trying to build an entire stack, just like GPUs, just like when we were building Grace Blackwell MVLink 72, just like now we are building a full stack system so that our customers could build amazing AI infrastructure. Let's take a look.
