Good morning. It's fantastic to be back in San Francisco at Build. It's always fun to be at developer conferences at times of great change. Developer conferences are always about understanding tech shifts, understanding the new stack, but it's also about really coming to grips with the new opportunity for us as developers, for the companies that we work at, as well as the broader world. So today we're going to unpack this. This conference is all about that. And if there's one key takeaway, it would be this: How do you all participate fully in this frontier intelligence ecosystem? It's not about any one piece of technology that you'll hear about or even the platform itself. It's about the value that you can build, you can compound, you can create on top of the platform. That's what a developer conference needs to be about and that's what we will focus on. So let's kind of get into it. Let's take a look at this AI stack that this conference is going to unpack in a great amount of detail. It starts of course with the compute fabric, that ubiquitous compute fabric that spans the edge and the cloud. You then have this layer which is the emerging layer where you have the models, you have context, you have the tools the models can access. And then on top of that you have the runtime where you deploy your agents and applications that you built on top of the context layer, on top of the model layer. And then of course you have the best tooling to do all of this and then you have the security, the compliance, and the governance. That's the simplest form of the tech stack, but let's kind of start where it always starts, which is infrastructure. And in fact, let's start at the edge with Windows. Because when you step back, the amount of compute there is at the edge is actually astounding. I mean think about every NPU, GPU, CPU, every PC, if you sort of aggregate that, that's a lot of compute power. So we asked ourselves one simple question: if we can deliver unmetered intelligence to every desk and every home, it takes us all the way back to the very beginning, but that's what we said, can we do that in this era of AI? And in fact, we're in some sense delivering that already today. When you look at something like say Outlook summarize, it's actually using onboard AI locally. Same thing with PowerPoint all text or Teams super resolution. It's just not Microsoft stuff yet. It's for example Adobe After Effects or Premiere are both using Windows ML across NPUs and GPUs for local processing. And so today, one of the things that I'm really excited about in order to tap into all this compute power is to expand the scope of Windows ML and Windows AI. You now have the full install base of GPUs that you can get to. So, I'm really thrilled that every developer out there can count on building for local onboard AI and then have it run across all of the install base. Now, we are also announcing two very cool new models that are all going to run on Windows in box. The first is a new SLM. It's sort of a more efficient model, Phi-4-mini-instruct, and it's a great reasoning model. And then we have the planning model, Phi-4-mini-planning, which is a local agentic loop. I mean think about it, you now have a full local agentic loop, you can give it tools access and build a fully onboard agent, agentic applications without having to round trip to the cloud. And of course we said in order to really push the limits, realize this unmetered intelligence, there's also a lot of hardware that's evolving too. And so we're thrilled to see the innovation across the Windows ecosystem. It's AMD with their Ryzen processors, the Panther Lake stuff from Intel is just exciting to see. Qualcomm announced two sets of things which are great. One on the high end with Snapdragon X2 Elite and then on the low end even for sub $500 PCs with Snapdragon X. So it's great to see all of this and of course that brings us to Nvidia and RTX Spark. This is a next generation SOC for PCs. It brings together the CPU, the GPU as well as the AI capabilities into a single SOC and that's the exciting part. And it includes unified memory architecture and also the integrated DRTM. So you now have all of the systems innovation and the SOC coming together. And we're really thrilled that one of the first devices that we built with the Surface Ultra, it's a beautiful device and it really brings the power of Nvidia together with the design and the craftsmanship of Surface. It's 128 GB of unified memory. It's a beautiful 2,000 nit display and it has an all day battery life. And so we're very excited to see this later this fall. And it's also wonderful to see all the designs from all the OEM partners who are building taking advantage of this new SOC and the new platform and bringing out some pretty exciting machines that's going to be all available come this fall. And so of course we said okay this is fantastic, what can we do next? So we said let's try and push this and push the architecture to its limit for developers. What if we could just max the compute, max the memory, build out that developer machine, that's the dream machine. And that's what we're announcing today: Surface RTX Spark Dev Box. Let's roll the video.
Yeah, this is it's truly a dream machine. It's got a one petaflop of AI compute. 20 CPU cores. All of those things have 128 GB of unified memory access. So super excited about this coming in the fall and you can join the wait list. I'm on the wait list as well. So we'll get there. You know, so then we said why stop there. We said what if we did one more thing. And in fact I think Jensen did that already. And he said Windows is coming to the DGX Station. So a new, I describe it as the desktop data center which you can have right there and running a one trillion parameter model locally. I mean just to sort of put it in perspective it's pretty close to what perhaps we had when we built GPT-250 or 300, right, when one of the first computer supercomputers. So, it's pretty crazy to think that we've come this far where you can now have a data center on your desktop. And of course, we're extending the developer endpoints even to the cloud. Windows 365 now has the developer distribution that's optimized for developer productivity in the cloud. So, that's the Windows 365 that I use every day. And it's simply about making Windows, whether it's on the laptop, the desktop, the cloud, wherever, the best place to build. And so to that end, we have tons and tons of updates. We're starting, by the way, with one of the favorite things for all of us, which is distraction-free dev environment. We're also introducing an intelligent terminal, which has got built-in GitHub Copilot, terminal width sort of the Copilot intelligence. And of course there's lots and lots of Linux love across Windows now. So you have 70 plus utilities, so Grep in full glory is now available for regular Windows access, 70 plus utilities as I said, all of that is coming to Windows. We're also bringing things from the Mac that you love, things like Starship, Zsh, Homebrew is going to be native on Windows as well. So you'll be able to switch to Windows. And today we're announcing WSL containers. This is I know been a point of pain when you're managing all these environments, switching, all the mental gymnastics of trying to keep this straight. So having first class support for containers will really help us be in the flow when you are building and deploying locally. And to show you all of this on our new Surface RTX Spark, I wanted to invite up on stage Kayla to just take a spin through all the dev tools. Kayla, go ahead.
Our team has been working hard to make Windows a great place for development and today I'm going to show you some major improvements and delighters you can try out today. So what we have here is the default experience on the Surface RTX Spark Dev Box. Right away it feels calm. There's no news feed, no widgets popping up, no notifications, and of course we're in dark mode. I'm immediately ready to start development. Well, there's one thing I'd like to change. I like to put my taskbar on the left. So, let me jump into my taskbar settings using the new run, which is built leveraging the architecture of PowerToys command palette. No, no, no. I want it all the way on the left. There we go. After popular demand, we're excited to announce that vertical taskbar is now available in Windows Insider builds. The new Surface RTX Spark also has a bunch of key dev tools already installed like Python, Node, and many more of your favorites. All of the developer goodness all in one file. And if you want to get the same experience today on your device, we're making this file available to everyone right now. We have a public repo set up with the configuration file and instructions for how to apply it using winget configure which will make the adjustments to Windows and install all the tools. Now, one cool thing that I have running here is PowerToys's new utility called Grab and Move which lets you hold Alt and move the window around from anywhere. Another tip is that you can enable End Task which lets you end the process without having to open Task Manager. So, let me jump into my dev drive. Dev drives run on ReFS with Defender running in async and are optimized for performance when it comes to development scenarios. Also, File Explorer is Git aware. We got stuff like last change author name, last change message, the status of each file. Plus, my favorite is that the branch name is on the bottom left. So, now let's get started building and open our terminal. This is an experimental experience called Intelligent Terminal that makes working with agents even more seamless. When you first install Intelligent Terminal, you're greeted with the option to pick your favorite agent. I'm going to use GitHub Copilot for today, but you can use whichever agent speaks to you. Now, here in Intelligent Terminal, I have a regular terminal pane at the top and an agent that's listening on the bottom, and I can work between them while the agent helps along the way. So, for example, here's an error being generated. My agent pane is able to detect it and provide a fix which is great when I don't remember the syntax especially for something like React. So I'm going to work on OpenClaw and I've already built it using WSL container. WSL container is a native container experience on Windows plus it can leverage the GPU which is perfect for the Surface RTX Spark. It can also reference your existing container files just like the one in the OpenClaw project. So, here's one of the files open in Microsoft Edit, which ships in Windows by default and just got syntax highlighting in its latest version. And then you can also just see your containers running with a simple container images command. And since we're on the topic of WSL, we're providing a WSL profile that's designed to feel comfortable for those of you who use tools like Starship, Zsh, and Homebrew. So, it comes preconfigured with all of your favorite utilities and it's available in the repo that I showed earlier and it also includes one of my favorites, btop. So, the Surface RTX Spark is designed for developer-heavy workloads including serving large local models for coding. I've already done some development with a 120 billion parameter model that most machines can't even load. So, here's a quick view of my usage and we can see how many tokens I've used locally. So, we're looking at about 3.4 million tokens leveraged on the device itself. Now, we can kick off multiple sub-agents using Fleet. And just so we don't have to watch me type, I'm going to use Copilot's voice feature, which is also leveraging its own local model. So, I'll just hold spacebar and tell it what I want it to do. Find any console.log or debug.log calls in the tray and node projects and convert them to the standard logger used elsewhere in the codebase. Here we go. Now the main agent will delegate sub-agent tasks of appropriate complexity to the local model utilizing my GPU and making it more cost-efficient. Now as developers while we're debugging we're often looking through log files to diagnose any issues. Sometimes finding the location of the log files is a challenge. I'd love to be able to just type something like 'gp log' and find them all. Awesome. So on top of already adding curl, tar, and sudo to Windows, now we're adding over 75 command line utilities like grep, head, tail, and touch for those of us who love to live in the terminal. So I found all my log files, but now having to parse them is the second challenge. Well, I've actually had Phi-4-mini-instruct proactively performing analyses on my log files this whole time. Now I can quickly diagnose anything that's gone wrong in my development. Plus, I don't have to worry about token usage because it's all local. We can even take a look at our machine's resources. The models are loaded and you can see 90 gigs of RAM being utilized by the GPU truly showcasing the full power of the Surface RTX Spark. We were able to use three local models simultaneously unmetered while going about our regular dev flow without a hitch. That's huge. So, I know you're going to love what the team's been working on. We hope it gives you a glimpse of what's possible on Windows today and where we're headed next. Thank you. Back to you, Satya.
Yeah, that's really the beginning of this idea of unmetered intelligence, having those models and having the agents using the models work in parallel to what you may be doing along with the cloud as well and that I think is what the platform enables as first class. Now let's move to the cloud. You know the driving equation for us remains the same which is tokens per dollar per watt. How do we optimize around this? So when we think about the systems problem we think about electrons coming on one end and tokens on the other end and how do we think about the systems optimization end to end. It starts with the data center design itself, the core compute, storage, network, all the accelerators that go into accelerating each of those components. How do you even think about the DC-DC connectivity and the networking as well as the offload to something like the local compute? That's the sort of systems challenge. But before we even get into all of the systems and the technology and the innovation, perhaps the most important design criteria for us is how do we earn the permission from the communities in which we're building these data centers? And that's where these principles ground us and focus us. How do we ensure that the DCs do not increase the electricity prices? Making sure that we are replenishing all our water use, creating jobs in the local communities for the local residents. Adding to the tax base, making sure we're strengthening the communities by investing in local training and the nonprofits in the area. Only when we live up to these principles, do the hard work around it is when we earn the permission to go ahead and innovate and build. And we've been doing a lot of data center buildout. Today Azure spans more than 500 data centers in 80 regions. It's the most expansive hyperscaler footprint out there. And we have added more data center capacity in the last 18 months than the first decade of Azure just to put that in perspective. But more importantly what we are building is also very different. In fact the first 15 years or so we built out the commercial cloud infrastructure for a set of heterogeneous workloads that spanned the enterprise. But now when you look at what we're building, when you think about all the gigawatts that were going to come online, really they have three dominant workloads: there is training, there's inference, and then there is the agent runtime. These are three dominant workloads. And in fact when you look at Fairwater, it was sort of our first AI super factory. It spanned two regions, Georgia and Wisconsin, the entire system was designed from the ground up for AI. We worked in fact very closely with even Nvidia on this and it's a two-story architecture that lets us essentially place racks obviously in three dimensions and pack the maximum number of GPUs densely with network access. That means you really got fantastic higher performance networking, lower latency and more effective bandwidth across the entire cluster. And we're rethinking even the power delivery. So we started okay how do we deliver hundreds of kilowatts per row while minimizing all the loss which is the conversion loss that happens from the grid to the silicon. So we basically sort of even took a new approach to it and all of this also changes with the cooling system and water. So in fact the cooling loop is filled once and the data center can operate effectively with zero water consumption. In fact, the daily water usage over the course of an entire year is roughly equivalent to what a single restaurant would use. And when it comes to the systems and the silicon, again we have a lot of choice. We have first party silicon, we have partner systems. We were the very first cloud to bring up in fact Nvidia's Vera Rubin system for validation. Very exciting to see that. We're working closely with AMD, we worked with them on MI300, now we're working with them on their next generation AMD GPUs. Maia 200 is continuing to scale. In fact, it's live in Iowa and Arizona. We'll deploy it internationally later this year. It delivers 30% improved tokens per dollar compared to sort of what's the leading GPU today. And we have validated it with GPT-5.5 and we are going to use that to power Microsoft 365 Copilot. And so when it comes to running these agents, the interesting thing now is it's no longer just about having an AI accelerator or a GPU. The CPU is really critical. In fact, the ratios maybe even coming to 1 is to 1. And that's why we are innovating with Cobalt. We're announcing the preview of Cobalt 200 VMs, our next generation ARM-based CPU design for both cloud-native and agent workloads today. So it's exciting to see Cobalt make progress as well. You know, one thing that we've been trying to make sure is that being optimized for these new workloads I talked about, these new agentic workloads. So in fact, Cobalt delivers 50 plus percent better performance than Cobalt 100 on cloud native. But we started benchmarking them using the GitHub Copilot traces, these agentic traces to see because the call patterns are so different. And we're now seeing 33% lower latency for the agent calls, 14% faster speed, 23% higher throughput. So this is about the core design of both the AI accelerator and the CPU for the agent. And of course when you talk about AI workloads you need scale, you need reliability and that's why the network becomes super critical. We have innovated with the MRC architecture and rebuilt how traffic across Azure moves to support AI workloads which are fundamentally synchronous data parallel workloads. And so therefore, they span tens and thousands of GPUs. And so you need to have them coherent. That's what the next frontier of is for us to make sure we're able to keep scaling the network architecture. And of course, it's not about just inside the data center. It's also about connecting across the data centers. And every Fairwater data center for example is connected through our continent spanning AI WAN forming this truly fungible compute fabric. And you know all this innovation is exciting but when you think about innovation designing for AI and I would say deep understanding of systems, there's no better company than Nvidia and there's no better person than Jensen to talk about it. So I wanted to invite live from Taipei, Jensen Huang, CEO and founder of Nvidia.
Jensen, thank you so much for being at Build again. I know it's late for you in Taipei. I really appreciate you staying up. You know, I've been looking at social and everything people have been talking about since your keynote over the weekend. And suddenly this concept of unmetered intelligence at the edge is so hot again. So maybe you want to talk a little bit, you have thought about this, talked about this, and now of course with RTX Spark really delivered I think what's a breakthrough system for AI to be much more ubiquitous. But maybe Jensen you can just share a little bit your vision around where you see this going.
Well, this all started about 3 years ago between a conversation between you and I and we were talking about how we could build a new class of PCs that's incredible for designers and creators and it would be incredible for artificial intelligence and it would be one of these systems that has the processing capability but also the software stack that's integrated into the world's design packages and creator packages and of course all the things that we're doing with AI. And here we are three years later. We built an incredible new chip and this system is supported by all of this new software that you've created for Windows and we now have the ability to have essentially an autonomous agent running on the PC. Now when you take a step back and you think about what does that mean for the 40 years or some 30 years we've been working together, we went from inventing DirectX together to creating now this incredible computer that has autonomous systems running. The PC evolved from being an incredible tool to now being a tool that's used autonomously by an AI assistant. And so the idea that I could be traveling and I'm on the phone and I could text my PC and ask my PC to get some coding done or some idea that I have and it would fire up the tools on the PC and it would make the modifications or the changes or the design that I told it to do and it would iterate with me while I'm away from the PC. My PC became an assistant while I'm sitting there. Of course this PC would be my great assistant as well. And so this idea that the PC evolved from a personal computer to a personal AI is just really exciting and to see it come to life Satya, to see it come to life and actually doing that, so I'm super excited about it. RTX Spark you mentioned earlier has all this incredible capabilities, a petaflop of AI performance. It has a petaflop of NVFP4. This numerical format that our two companies worked on together that allows us to take advantage of this 128 GB of memory and fit maybe a couple of hundred billion parameter model. A couple hundred billion parameter model is state-of-the-art. And so I think the days of having a really smart assistant running autonomously on the PC is here.
Yeah. No, it's so awesome. And in fact, I'm also excited about Windows coming to the DGX Station. And so that's another thing that it's kind of like data center right on your desktop and it's so exciting. But talking about that data center side, obviously this entire thing got started when we built the first supercomputer together to train the GPT models and we've come a long way. In fact, even I was talking about the Fairwater design. It was custom built essentially for the Grace Blackwell era to be able to max the data center design with the system design you had and now of course we're validating Vera Rubin. We're very excited about it. Maybe you want to share a little bit about sort of what happens even on the cloud side with how you're pushing on the systems innovation.
Well, our journey is incredible. We built the first AI supercomputer together that was based on Ampere. Of course, Hopper was an incredible success. Those first two generations were focused on pre-training. Grace Blackwell came along and all of the focus moved to post-training, reinforcement learning which allowed us to have reasoning models and these reasoning models based on mixture of experts were incredibly intelligent, energy efficient. But it requires giant systems and so we created NVLink 72 and the entire rack became one computer. We had evolved from one node to now one rack. Well, Microsoft deployed the largest number of Grace Blackwells in the world today. The fastest and the largest number of Grace Blackwells in the world. Fairwater is just a magnificent system to look at. It's just a miracle of engineering. It's just an incredible feat. It's completely liquid cooled. You mentioned something earlier that I'm very proud of as well that it's closed looped, basically uses almost no water, and it's incredibly environmentally friendly. It's energy efficient. We're able to increase the token generation rate and reduce the cost of token generation by an order of magnitude, some 30 times over Hopper. So that was a huge achievement. Well, Vera Rubin was created for a world where these AIs are now agentic. And so whereas Hopper was created for pre-training, Grace Blackwell for training, post-training and also inference, Vera Rubin is designed to run agents. It's agents as you know. This computing pattern is exactly the same computing pattern we're going to run on the RTX Spark. It's exactly the same agentic system except of course it's going to be much much larger. We're going to process enormous number of them simultaneously. Many of them are going to be from different customers and different partners. And so the entire path, the entire coding path from storage, which is the long-term memory, the working memory is encrypted. The data is encrypted in transit. The data is also encrypted in use. And so we're going to really innovate in the area of confidential computing. And so this entire disaggregated distributed computing system you mentioned CPUs. Vera is a revolutionary CPU designed for agents. You know the past CPUs were designed for humans and we're just more patient than agents are and agents want low latency just as you've been working on as well. Vera was designed for extremely low latency and so Vera Rubin is just completely revolutionary. I can't wait to show it to everybody. You've already stood it up.
Our two teams have been working very closely, almost long before the chips taped out. Long before the systems were brought up, our two teams were already completely aligned. And so the design, the data centers were created for Vera Rubin. The Vera Rubin is designed and integrated into your complete stack, into your networking, into your security. And so the moment that our systems were rolling off the lines they were being stood up at Microsoft. So I'm incredibly excited about the collaboration.
Yeah, I know this speed of light execution between the teams is fantastic to see. And of course, all this is to power the ecosystem around us, right? I mean, you and I having grown up with the PC, the server, and now with AI have always thought about ultimately it's about creating the opportunity for every developer, every organization to build on the work that we do and the platforms we create. And speaking of that, there's a lot of software that Nvidia builds that's all also coming. For example, we're going to have your models in Foundry, your tooling in Foundry. We're going to have in fact your software even help us with accelerating our workloads when it comes to even the data warehouse. We're going to obviously have stuff in Windows. Just talk a little bit about that broader vision of what does it mean for as an opportunity, right? Because everybody talks about this one model or one piece of tech, but it's about the broadest biggest opportunity for people to create value. Maybe you want to share a little bit about that.
Well, we've been preparing for this moment. You know what happened in the last several months? We've been working for a decade and a half together getting ready for really what happened in the last several months. All of a sudden, because of agentic systems, the convergence of these really great models, AI is now useful. If you just look at GitHub, the commits into GitHub has gone completely parabolic. In the last several months, the amount, the number of commits increased by a factor of three. It's clear that agentic systems are useful, that it's doing productive work and also tokens are now profitable as a result. And so the amount of demand for compute between the usage of the AI and the computation that's necessary for agents, the compute demand has really gone through the roof. Well, one of the things that we've been doing together is making sure that all of the tools that the agents are going to use are fully accelerated. Fabric, for example, is now fully accelerated. We're accelerating data processing, SQL, Spark, semantic-based, vector-based, graph-based, we're going to make sure that all of the tools that are available on Azure are going to be fully GPU accelerated because the agents are going to be impatient. The faster we can get the answers back to the agents, the faster they can iterate, the faster we can generate tokens which are ultimately what the developers, both of our customers would like to do is generate a lot of tokens that are really profitable that are highly intelligent.
Thank you so much Jensen for the partnership and the leadership and the innovation that you bring to this entire ecosystem and really thrilled to be working closely with you and the team and bring all this to the developers here and beyond and look forward to seeing what the next sort of few months and the next year bring in terms of the innovation that gets built on top of the platform. So thank you again for joining this late in the night from Taipei.
Thank you so much Satya for your partnership and friendship. Thank you.
So, so far we've talked about the edge and the cloud. The current form factors, right? I mean, when I saw that Jensen picture from the weekend where he had all the desktops, I felt like, man, I'm back in the '90s, right? because it was so cool to see the lineup of all the machines that I loved and I grew up with back yet again with new functionality, right? The same form factor but unbelievable new functionality because of the onboard AI capability. So that's sort of what we've seen with the laptop, the desktop and of course with the cloud. But it also sets up that next question. If you have that capability which is new function and you can put it into existing form factors, can you even purpose build new form factors for the new function? Can you build a new platform even for the agent era? And that is the motivation behind Project Silica which we're introducing today. And to talk about this I wanted to invite Stevie on stage. But first, let's roll the video.
I am so excited to be here. It really is great to be back on this stage. Now, before I talk about those awesome new devices you just saw, let me start with the why. Back at Build 2023, I talked about the outside AI application structure where AI moves from operating within the application frame to operating globally, working across multiple apps and services to connect, coordinate, and maintain context across entire workflows, devices, and time scales. What if there were an ecosystem of devices specifically designed for that new type of application structure for those types of agents for that transformational interaction technology? That is the impetus behind Project Silica. But with so many possible forms, which one do you pick? What is the next device? You see the big aha for us is that it's not about choosing one specific form factor. It is about creating a system that extends your agent across a constellation of devices. The next computer is not one device. It is all these devices working together as one system with agents showing up closer to where and when you need them. To realize this vision, two challenges immediately show up. First, many specialized form factors already exist, but often rely on custom one-off apps and fragmented stacks that are difficult and expensive to build, deploy, and maintain. And second, across every industry, people and organizations are already building their own agents deeply specialized and instrumented for their work. But the impact of those agents is constrained by how and where they can exist. Project Silica addresses both by giving organizations a way to extend their agents onto new purpose-built, easy to manage form factors designed to reach the nooks and crannies where conventional computers either do not exist or are not optimal. It's a turnkey solution for building unique agent-first devices enabled by three pillars. First, it's enterprise ready enabled by the AOSP-based Microsoft Device Ecosystem Platform. Second, it has an agent-driven interaction model with just-in-time UI that adapts to the form factor. And third, it has extensibility so you can bring your own agents. And tying it all together is Azure unifying the system across cloud and device. Okay, that's enough of that. Now, let's talk about the devices. Today, we're previewing two very broad categories. The first is stationary and the second is portable. The first device is designed for your desk and it's built on MediaTek silicon. With Hello for Business, just walking up to the device securely signs you in, giving you direct access to your agents, just like Nathan's about to show you here. For the information worker, this means frictionless yet protected access to Microsoft 365 Copilot grounded in Work IQ. And with a simple glance, it services what matters next in your workday, helping you think, plan, and even act by delegating tasks to your agents with a simple tap or just using your voice. Think of it as a dedicated secured ambient device for work. It even supports experiences like handoff between devices, acting as a companion to your existing Windows PC. Or it can even let you access your cloud PC through Windows 365 and a connected monitor. How cool is that? Now, the second device is portable. It's reimagining a wearable that millions of people use every day. The access badge built using Qualcomm silicon for wearable. This digital badge is a lightweight form factor designed for agent interactions on the go and that it's adaptable across a variety of verticals and workflows. All right, I have here an early prototype of the badge. And so using my fingerprint I tap to unlock the device and I have access now to all my agents in a secured manner. And would you look at that? I already have a task and it says gather content for your social media post for today. So why not just do it right now, right? So I'm going to hit record and then now the device camera is recording. I'm going to pan across. I hope you don't mind. I'm going to take your shots. Yes. Thank you. Copilot, find some good shots from this, clean them up, and then send them to me for me and my team to review. All right. And then there you have it. Now my agent's off running through multiple tasks to actually clean this up and send them to me and the team. That's pretty cool. Okay, I know it's a simple demo, but it's all agent driven and there are so many verticals, so many opportunities. I mean, for example, imagine in healthcare, from the moment you pick up the device, the right agent shapes the experience around the role and the workflow, helping with check-ins, patient records, and critical insights, all through enterprise-grade secure access. And with the built-in microphones, the nurse can start a hands-free voice-based documentation, including dictation and annotation. And the side-facing camera can be used to verify and document patient vitals or even scan in medications and help verify workflows. You know, these are just a few examples of how this small purpose-built wearable can bring intelligence directly into the flow of patient care, helping nurses access and gather and even act on information while staying present with the patient. And while both the stationary and portable concept devices represent a specific expression of agent-first devices, their core reference hardware and software are designed to be highly flexible. I mean just with a few changes, loading a different agent, adjusting the shape, the screen size, the sensors or even input methods. The same foundation, the same software can be adapted for many verticals and workflows such as retail, industrial, hospitality, financial service, legal and so forth. I mean that is the power of the platform, that flexibility. So whatever your scenario, there are thousands of untapped opportunities to bring agent workflows into places where computing has not naturally fit before. And while this is an early look, we're really excited that AccuWeather, Best Buy, CVS Health, Levi's, Target, and others are working towards exploring how specialized agents and devices can improve their workflows. This is the broader opportunity for the
Ecosystem. Agents moving outside the app and taking shape in devices designed for a specific scenario, a specific customer, and a specific place. And for all of you, this is the moment to imagine where your agents should live, what form they should take, and what new work they can unlock.
Last week, Satya sat down with Cristiano Amon from Qualcomm to discuss his future. Let's roll the video.
Cristiano, it's so wonderful having you here at the device lab and talk about these new reference designs.
Very happy to be here. I have been to this device lab a lot recently.
You know, one of the things that you and I have chatted for a while is how there's a real platform shift. We're moving from building operating systems, devices for apps to agents. You want to talk a little bit about how you see this?
Yeah, absolutely. And every generation of technology transition with the shift and I think this one seems to be a very big one. Agents really changes the whole nature of the device in itself. Starting with the fact that if the AI understands the world the way we understand it, it's going to be closer to our senses. It's going to be closer to our eyes, to our mouth, to our ears. It's going to be things that we wear and it's changing the nature of the computing. I think you need a computing that is now geared towards real-time context and from silicon to cloud.
I mean, this is sort of one of the reference designs we have which I love. There's stuff happening at the edge here on this device. There's things on the cloud. You want to talk a little bit about the core systems and silicon implications for such a new ecosystem?
You need a very power-efficient CPU. The whole silicon is designed for you to have a cloud-native experience and then you have a lot of sensors, right? A lot of sensors for context. It's a much more personalized and bespoke experience than an app in itself. And I think that's a change in the nature of devices and even the definition of a wearable platform is changing in itself and you've started to see those incredible new form factors that computing platform.
One of the things that you and I have talked a lot about is how do you build it such that there is an open ecosystem, right? Because it's not about one agent, it's about any agent. This is what makes it very, very interesting and exciting because the smartphone today is at the center of your digital life. The job of those devices today is to be extending the functionality of the smartphone and because of that you actually saw that many of those platforms became vertical. It was a natural thing to have a vertical platform from the same company because the phone was at the center. That changes dramatically, right? When you think about agents and agents becomes the center of your digital experience and I think the industry is going to be looking for an open horizontal platform that actually enables the agents to be interacting with the best possible device for different applications and I think that's what's exciting and we want to make it possible for anybody who's thinking of an agentic system to not just be bound to current devices but to imagine that there can be many, many devices that carry that intelligence in different contexts. And so that's the open ecosystem we want to build together. And again, it's so great to be partnered with you to get this started.
That's awesome. We're very proud of this partnership and it's just the beginning.
Thank you very much, Stevie and Cristiano. We are very excited about Project Solara. It's sort of a new platform, but perhaps more importantly it's a set of new platform rules that don't, in some sense, hem in what you can imagine the type of form factors where your agents live. I think always whenever these new platforms have come, you get to rewrite even the rules of how new platforms operate and that's what we're trying to get done with Project Solara so that you as developers and enterprises have the flexibility to imagine the form factors that you want and have your agents be ubiquitous. Now let's go up the stack to the next layer.
We are building a new intelligence layer bringing together the models, context, as well as the tools. It of course starts with model choice. Every customer, every developer is going to choose the right model for the right task and their eval mix, latency budget, even COGS budgets. And Foundry today has over 11,000 models. It's the largest model catalog out there from OpenAI to Anthropic and of course even MAI models. And just last week we brought the OpenAI real-time voice models along with Claude Opus 4.8 to Foundry. So we're continuing to bring all these frontier models. The consideration though about the models is becoming now increasingly key. In fact, from the last developer conference to now, when you're building any agentic system, having this context really shaped right is becoming super important. And in fact, it starts right at the data tier. Right? The data state to date has been built for applications that supported these user-facing applications. Right? Now you have to change and build them for agents which again have very different call patterns even to the data tier. Agents are continuously storing, retrieving, reasoning, acting and learning, right? That's sort of what's happening in a continuous loop. And you see that today agents are using things like Cosmos DB for their memory. In fact, ChatGPT does. Azure Search is used for retrieval of indices and embeddings. Fabric IQ for the semantic models and the ontologies, it's kind of the business logic tier effectively for agents. And also you have Fabric Real-Time Intelligence for all the observability traces. Which now brings me to a very exciting new service, Horizon DB, which is our fully managed PostgreSQL service on Azure. Really thrilled to have this.
I mean, one of the things we wanted to make sure is we built ground-up a PostgreSQL managed service which was for high availability, scale-out. It's zoned redundant with automated failover. 128 terabytes of storage per cluster, 15 read replicas. I mean, the read-heavy workloads you can scale with this managed service. And in our internal testing, we're seeing something like, you know, the Horizon is delivering 3x throughput compared to any self-managed PostgreSQL setup. So it's super critical when you think about the scale you need. The other data workload we're also changing pretty dramatically is the data warehouse. In a world where agents are constantly querying data, the data warehouse effectively becomes pretty mission-critical, right? I mean, it's one thing to have it be mission-critical for users. But when agents need the analysis done on the fly, bringing GPU acceleration to Fabric is super key and we are seeing 7x performance gain. So it's really thrilling to see that AI acceleration.
So if you sort of have the data tier, the layer above this is the IQ layer that we are building that brings together essentially the model capabilities along with the data, right? So you kind of mix the data and the model capabilities so that you can deliver that right context to unlock intelligence, right? That's where in fact, if you want, when people talk about token efficiency, this is perhaps the most important consideration, right? If you structure the context right and feed the models, then you will by definition going to be so much more token efficient. The first domain is the web. Web grounding is so important. You need that fresh, high-quality and fast web data. And that's why we are really, really excited to announce today Web IQ.
And Web IQ is built on our global infrastructure that's already serving over a billion users but fundamentally rearchitected for the LLM and the agentic workflow. It's model agnostic. It's MCP native. Plugs right into any agent runtime. It has web, it has news, images, video. So agents can ground responses in fresh, verifiable content. And Web IQ leads across all of the three key criteria, right? It's best-in-class in quality, it's best-in-class in speed as well as in cost. So we're very, very thrilled about Web IQ being in the developers' hands as you build out agentic systems. Of course, we're not stopping there. Beyond the web, every developer wants to be able to ground their agents on what's the most valuable data across the enterprise. And so we are bringing together Foundry, Fabric, and Microsoft 365 as this unified IQ layer. Right? It's a continuously updated understanding of your organization. To show you all of this rich IQ in action, building real-life agents, here is Elijah. Elijah, take it away.
Agents are only as good as the context we give them. Microsoft IQ unifies enterprise intelligence for every organization. I'm here at a power utilities control center and we'll start by running a long-running agent. This agent is going to help us assess the current grid operations incident and produce a brief for us so we can respond accordingly. Now I'm going to go ahead and kick that off. And while that runs, let me show you how we got here with context from Microsoft IQ. We built our agent in Microsoft Foundry. It's connected to various tools and it's also wired to a Foundry IQ knowledge base, a single grounded source that packages our documents, operational data, and people into context the agent can reason over. After building the agent in Foundry, we published it to Microsoft 365 for the whole team. Let's go see it in action.
Now, here in M365, I will start with a question about current events that an ungrounded LLM wouldn't be able to answer. I'm going to ask about current electricity prices in SF. For this, our agent pulls in the first IQ in our toolbox, Web IQ, search built for the AI era. Web IQ delivers industry-leading quality, velocity, and efficiency. Web IQ constantly indexes fresh official sources from across the web. And additionally, Web IQ does a great job with semantic documents. And here we can see Web IQ gave us our answer grounded in reality.
Now, not only can we deploy our agent here in M365, but we can also embed it in our custom apps. Let's head back to the control center. Let's see how we handled a previous incident. So after using Web IQ to gather external info, we ask for details about our potentially at-risk substations. The real power of Microsoft IQ comes when we combine that external knowledge with our own internal enterprise context. For this information, our agent pulls in the next layer of Microsoft IQ, Fabric IQ.
Here is Bright Line's grid represented as a Fabric ontology, an operational model of the live grid. And critically, we didn't build this from scratch. Fabric takes the Power BI semantic models used by millions of customers today and lets teams extend them into rich ontologies that help run the business. This model, yes, this model is coupled with live telemetry. So, it reflects the real operational state of the grid minute by minute. This is what Microsoft IQ means by enterprise intelligence.
Not data scattered across disparate systems, but a single living model of the business that an agent can reason over. Now turning back to our agent, we can see that it gave us a table of our most exposed substations. That's the agent querying the ontology you just saw. Now the agent has access to both the outside world and our grid. The last piece is the one that turns a situation into a response. Our policies and our people. By asking, 'What are the steps to respond to a substation trip?' We activate the final layer of Microsoft IQ, Work IQ.
This is Bright Line's response procedure in SharePoint. It's the playbook the team actually reaches for when something goes wrong. And the important thing is the agent isn't working from a stale upload or copied snapshot. It's answering from the same source the team maintains day-to-day. When the procedure changes, the answer changes with it. No re-uploads, no prompt rebuilds, and no stale versions. And critically, this is your knowledge. The assets you create stay with you no matter what model or agent is reasoning over them. Now, if we return to our agent, we can see the proper way to respond to this issue. Not a generic recommendation, but our playbook applied to this incident. Now, we just saw one situation, three questions, and one connected answer. Now, let's go check back in on our long-running agent.
And oh, let's just check the backup really quick. Moment of truth. And boom, our task finished.
Here we can see every step the agent took. First, beginning with Web IQ, connecting it to the outside world. Second, Fabric IQ through Foundry, anchoring it in the real state of our operations. And third, Work IQ grounding it in our people and procedures. I triggered this manually, but with Foundry routines, this can run on a schedule, turning a one-off response into continuous proactive execution. And if we look closely, we can see it even used the power of Work IQ to alert me directly about the incident. And if we go ahead and check Teams, it sent me an incident brief notifying me of the situation.
That's the power of Microsoft IQ. When a crisis hits, the team doesn't chase answers across a dozen systems. They ask a question and get a response grounded in the world, their operations, and their people all in one place they can trust. Back to you, Satya.
All right. Thank you, Elijah. And now, you know, let's sort of move up from this context model layer to deploying these agents and thinking about the runtime. And you know, when you're building a first-class agentic system you need a first-class agent runtime and a platform that we are going to ship both with Windows as well as part of Foundry in Azure. We want Windows to be a fantastic place to run and scale agents. And agents effectively are a new execution environment, right? It's a new paradigm even. They reason continuously. They generate and run code dynamically. They take actions across files and devices as well as across the network. Obviously there's a lot of power in it, right? The fact that it can generate code and act on it on a long-running agent that's autonomous but obviously it creates new risk. And that's why today we're introducing Microsoft Execution Containers or MXC. MXC is a new policy layer that lets Windows apply isolation and containment using OS-native primitives. Right? You need to bake this into the operating system so that the containment is enforced by policy. You can have process-level isolation for lightweight agent actions. You can have session-level isolation for user separation. So Windows and Linux virtual machines in fact are also great including WSL for much stronger boundaries. And in fact if you want full isolation and containment, Windows 365 for agents for maximum isolation in a separate managed environment. Effectively, you can pick the right containment option for the workload and Windows will enforce it via MXC. And so I think this becomes pretty critical as you think about deploying agents at scale on your Windows desktop. We want to ensure containment is enforced of course regardless of who builds the agent, right? So, this is why you want to bake it into the operating system. To that end, we're working with many partners to ensure that containment we are building supports real developer workloads out there and also addresses the needs. In fact, NVIDIA is bringing OpenShell to Windows to securely execute autonomous AI agents. And today we are really thrilled to announce that OpenClaw runs on Windows leveraging MXC.
Yeah, we are very deeply engaged with the team to make OpenClaw run super well on Windows. And so let me hand it over to my colleagues Scott and Samantha to show you OpenClaw on Windows. Scott, Samantha.
Hey friends, you know, OpenClaw came out in November of last year and it took the world by storm. And for the last several months, I've been using my OpenClaw to stay on top of my health. My claw can help me manage my blood sugar and it gives me even proactive notifications via heartbeat. I've got mine triaging my personal email. It's doing my GitHub issues and tracking packages and it even buys me movie tickets. So Samantha, what are you using your claw for?
That's cool, Scott. I turned my OpenClaw agent into my triathlon coach. I'm nowhere near ready for my race in September, but Coach Claw developed a workback plan for me and is using my Strava data to notify me on how I'm progressing and to keep me accountable when I'm slacking.
Now, we've both found our claws to be super useful, and that's why we're working closely with OpenClaw to make them successful and even more successful on Windows. We've been collaborating in the open on GitHub to bring you all an OpenClaw Windows companion app. It's going to help you set up your own claws or connect to existing ones whether they're hosted in Windows or in WSL. And the Windows companion, we're going to sandbox the OpenClaw tool calls to keep you and your system safe.
Yeah, you'll see the OpenClaw Windows companion app running right now in the background. Go ahead and right-click on it, Scott.
All right, that looks awesome. You'll notice immediately it looks like a native Windows app because it is. It's written in WinUI3. It's got all kinds of information about my gateway, other machines that are participating in my claw, my sessions, and my usage. I've also got quick access to things like chat, canvas, the main dashboard, and more. Let's jump into companion settings.
Within the app, we've got full chat support with tool calling. And you'll notice down here in the corner, we've got lots of permissions options along with our sandbox configuration.
Now, this sandbox is really interesting because this is using MXC, the Microsoft Execution Containers. And for this, we're going to be using process isolation. Now, newer versions of Windows are going to have even more containment options. So, you're going to want to keep an eye out for more news with MXC in the future. Now, I can see that I've got one-click security option settings, but Samantha, talk to me about custom folders.
Yeah, you've got full support about what files and folders you want OpenClaw to have access to and really granular security features like clipboard access or talking to the internet itself. Now I've given it read-only desktop read access to your desktop folder. OpenClaw already has a rich safety layer and that layer is only augmented more by appropriate containment that can be managed by me or policies applied by it. Now, for the purposes of this demo, I'm going to do something really scary and ask OpenClaw to delete all the files on your desktop.
That's cool, because I have a nice clean desktop.
Um, I think you're keeping a secret. You hid all your icons from the audience while we were on stage.
