Microsoft Build 2026 | 開場主題演講

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. There 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 'grep 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 awk, 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 above our regular dev flow without a hitch. That's huge.

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 3 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 a 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 as 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.

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 is 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 and 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.

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 Satya for your partnership and friendship.

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 and 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 Accenture, 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 vision. Let's roll the video.

You here at device lab and talk about these new reference designs.

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?

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?

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 they 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.

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, WebQ 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 Lines 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 PowerBI 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.

Hey friends, you know, Open Claw 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 Open Claw 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 open claw 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 straw 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. Open claw 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 gonna 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.

That is... I think we need to show the audience who you really are.

So disrespectful.

I know where everything is. Just don't touch my stuff. I know where they are. You know, Sam, I need to make sure that you're clear that a messy desktop is an organized mind. I'm pretty sure that that's the quote, right? So, what we've done is we've asked OpenClaw to delete those files from the Windows node. And the only thing that is going to keep from happening is MXC because we've turned off all of the many layers that OpenClaw offers, but our it in this case Samantha has set it to read only. So, it's trying to go and delete all of those files. We can actually see the different attempts where it's going and deleting and then checking the directory and then deleting again because it's very persistent. It wants these files gone and I want them to stay. Nope. The readonly sandbox is there. 94 JPEGs are still on the desktop. Absolutely. My desktop icons are safe from Samantha's reign of terror.

Foiled again.

Oh my goodness. So bad. Today we've seen security sandboxing, WinUI3, and open-source brought together all in this alpha release of a Windows companion app. We think this app is a great opportunity to showcase OpenClaw on Windows, and it's only going to get better in the coming months.

That is right. And by the way, I want to note that we're doing all this development work on this calm Windows development machine with all the tools that I love like WSL, containers and containment and I've even got GitHub copilot with multimodel support ready to go. Now, this is literally how the team and I have been working on the OpenClaw app on GitHub. Now, people might wonder how all of this came together. Turns out that over the holidays I got a DM from this random guy on the internet and it took me a couple of days to get back to him. And then when I did we just had this kind of cool idea that maybe he should come to Microsoft build. Now I think there's one more person that we all want to thank for bringing the next generation of agents to world. Everybody put your hands together for the claw father himself, Peter Steinberger.

Samantha, he's just showing off my secret DMs. Um, I'm so excited to see Open Claw native on Windows. You know, watching a claw try to delete all your desktop file and just fail made me really happy because 6 months ago that totally would have worked. You know, I built OpenClaw to have access to everything, you know, my files, my machines, my chats, always on and fully open source. That's what makes it so powerful. And that's what also makes companies a bit nervous. You know, what I kept hearing was, Peter, I love my claw. Can I use this at work? And that's what we spent the last few months on with Microsoft, GitHub, OpenAI, Nvidia, just to name a few. We added observability. We added auto mode for permissions. We changed how access works. It's not all or nothing anymore. You can pick which folder should be read only, which one should be writable or hidden. So here's the news. You can totally run OpenClaw inside YOUR COMPANY NOW.

AND WE EVEN made the harness itself a plugin. You can bring your own copilot, codeex, whatever you already trust and your rules come right with it. And then you put open claw on top of it. You get persistent memory, heartbeats and you get a claw right inside or teams. It's been really exciting to see OpenClaw grow into something much bigger, a global movement and a community. And I started the OpenClaw Foundation, a real nonprofit, so it stays open and neutral, any model, any operating system. Because we are entering a new era of building with agents. More capability for the people who don't code and more power for those who do. And we get to do this. We get to build it together in the open. So my task is simple. Come build with us. Thank you.

Hello everyone. I'm so excited to be amongst my fellow devs today. This has been a long day. Fix your shoulders. Sit back. You're right. I see a lot of you sitting up. Great. Great. I know you're drinking from the fire hose of information today. So, I want you to go into the next few minutes thinking, what can I try out on my laptop later today? So, as Satya said, we're going to show you Rayfin. But first, I can't wait to show you the new GitHub Copilot app. This app is your home base for development and operations on your computer, and we think you're going to love it. So, let me show you around. When you open up the app from the start, you see this home screen here where you can kick off a new agent coding session. But also, before I get into the serious stuff, you can drag Mona around and there's a game. Look, it's so fun. Okay, I'm not very good at it. So, let's just get back to you can kick off a new agentic coding session.

So, I started off one a little bit earlier here and it gave me a review of a bunch of release blockers. Which one should I fix? Call it out. 8 three the critical ones. You know what? How about we just do all of them? Let's go. This app will now kick off a separate session for every single issue here. I don't worry have to worry about stashing or coding complex or anything because the app takes care of that with git work trees. Git work trees are isolated environments for each session that you run. So your agents can work in parallel without stepping on each other. But you still have to merge them, right? So, Copilot has your back there, too. If I head over not to this one, but to this issue here, I can run agent merge. And when I enable agent merge, Copilot will continuously babysit this PR through CI checks, code review, and merge conflicts. Okay, let me keep showing you around while those are still running. Now, if I head over to my work, I can see a focused view of all of my activity and just projects loaded in the app, issues and PRs, everything here. And then under automations, I have a bunch of reusable sessions and workflows that can run locally or on the cloud. You see there's issue poetry there that is real and that is loadbearing. Okay. And now under sessions, like I briefly showed earlier, these are sessions. If I want to add a new repository, I can click that button here and it can pull from a local repo or from a GitHub repository. And then if I were to just add one, I can add a session in Pocket Cow. This is an open-source repo. I can start a session anywhere. And it just loads it. I don't have to clone. I don't have to pull. It just works. Now I when I look at a session within this repository, let me look at this other one over here. I get an integrated browser. There's a terminal. I can see the chat. It's all loading. I can even toggle light mode and dark mode in here. And there's also this great button, pick and polish, where if I click that, I can pick and polish anything in this app and it adds it to the chat and I can say, 'Hey, I want you to add reordering to this list and it'll just work all living in there.' I have access to all the most popular models via my single GitHub copilot subscription, including those from OpenAI, Anthropic, and Google. You can see all of them in our model picker. And having model choice is great. Not only can you pick the right model for the task, but for bigger features, Copilot can request a rubber duck review. So in this session here, for example, I was using GPT 5.5, but if I scroll up, it actually requested one from Claude Opus 4.8. All models have blind spots, and the power of the copilot multimodel approach means that I can catch them earlier. And this is all very cool, but working with AI in 2026 should be more than just chat. You just saw me scrolling that. There's so many words here. So today, I'm very excited to show you the concept of a canvas. And I'm going to open one right there. The canvas is how an agent can build a custom UI to communicate with you. What if your AI could see? Everyone say demo gods. Bless us. Okay, let's see if it works. Here is a fun canvas where if I get the camera going. Okay, the agent shows your PRs down here and I can take toggle it with a thumbs up or a thumbs down. Let's approve it. Hey, it's so fun.

And they could go so much deeper if you want. This is just the beginning of what you can do. So again, I kicked off a bunch of different sessions earlier. This is a signal box app. It's 100% agent-built. It's containerized with a database backend. Would you be able to deploy this to your enterprise with no questions asked? Be honest.

No. Yes. Exactly. No. But you can with Rayfin. And that is very, very exciting. Let me open up a new terminal over here. All I have to do is type Rayfin up. And then demo gods bless us. Come on. It will maybe deploy Blammo. It's happening. Yes. And all hosted on Microsoft Fabric. Okay. I know that's a lot. I know that's a lot. With Rayfin, your agents get a complete enterprise backend. So, you can deploy with confidence in the way that's best for you. But this is the key thing to remember. This app is not just another session manager. Yes, it is. It manages a lot of sessions but session managers just make it easy to create work but GitHub copilot helps you to finish it. Thank you so much. Happy Pride. Back to you Satya.

Everyone is building agents, but that's not the hard part anymore. The hard part is integrating them into your business and governing them at scale. Today, I'll show you how Foundry makes this easy. Let's start locally. I've already built a Langraph agent and now I'll show you the value Foundry adds. First, tools. Agents need tools to get work done. And with Foundry Toolbox, I just add my tools once and any agent can consume them through a single MCP endpoint. And because tools live centrally, that means governance does as well. I've applied a guardrail which blocks PII from leaking into tool calls and tool responses. And all I have to do is apply this once and all my agents are protected.

Now let's make this agent enterprise ready. To deploy this agent to Foundry, all I have to do is add this one block of code and then I push my changes and GitHub actions takes it from there. Once deployed, I can actually use this agent in the same Foundry extension I showed you earlier. And let's now test it out. I'm asking it to track a few open items from a standup I had earlier today. And what's actually happening right now is Foundry is spinning up a dedicated microVM just for this session. And the session even gets its own persistent file system. What you're going to see in a minute here is if I go to the files tab to track the open items, the agent's actually writing to a file. Pretty cool, right? Plus Foundry now has server-side traces and built-in evals to show me exactly what happened on every run. But how do I know if my agent's doing a good job? That's what Foundry's brand new rubric evaluators are for. It's super simple. With just one ACD command, Foundry reads my agent and then generates the evaluation criteria for me. In other words, it creates a rubric personalized just to this agent. Now, let's check out this rubric in Foundry Portal.

Look at these dimensions. Ground governance, outcome correctness, prescribed source usage. I didn't write any of these. Foundry generated them from production traces. And with this rubric, I can score my agent and run evals. But we can do so much more than that. That's where Foundry's brand new agent optimizer takes over. Here's how it works. It tunes four things. The model, instructions, tool descriptions, and skills. And then it generates improved candidates and scores each one using the rubric I just showed you. Here I can view the candidates. I can see the strategy used. I can see their scores. And I can actually view exactly what changed. This candidate, for example, improved its score by updating the model and the system prompt. Foundry then makes it super easy to deploy the best candidate as a brand new agent version. But this is not a one-time thing. Every run feeds the next eval, and every eval tells the optimizer where to improve next. So your agents now get better the more they're used. But now, let's put this agent to work. I've published my agent to Teams and M365 Copilot. And right now, I'm going to ask it to just catch me up on what I've missed because I've been offline all morning, but my agent hasn't.

While it works on that, though, let me explain what makes this agent different. This is an autopilot agent, which means it has its own identity and productivity license, so it can work across M365 on its own behalf. Earlier you saw me using this agent by myself, but shipping a feature in a new release takes a whole team. And that's why this agent lives in our team's group chat. Now, let's check out how it did. It summarized all updates and called out the key features I need to be tracking. But to put an agent like this actually to work in your enterprise, governance needs to come first. And that's why every autopilot agent requires admin approval. Here, admins can review all critical details and they can even choose who has the ability to talk to the agent. But it doesn't end there. Even after approval, admins can continue to monitor the agent and block it at any time. But governance cannot apply to just one agent. Every agent in your organization needs to be managed with the same rigor as users, apps, and devices. And that's exactly what Microsoft Agent 365 provides. So let's zoom out. Today we saw Foundry accelerate development, take your agent from local to enterprise ready, and put it to work in M365. Foundry makes it simple. You build the agent, we handle the rest. Now back to you, Satya.

Thanks, Satia. Now, we all know that security scans can take a while, so I'm not going to do one live. So let me show you the results of an M-dash scan I already ran on my code base. Now, the system runs as a standalone CLI, but today I'm using it in my GitHub copilot app on my local dev machine. The scan is broken down by vulnerability, domains, and severity. And it's also an additional to finding traditional issues like coding errors and hard-coded secrets, it's also identifying AI specific vulnerabilities in the code.

Now what happened under the hood is that over 100 specialized agents are working together to discover, debate, and prove exploitable vulnerabilities end to end. And when the scan finishes, it generates both a SARIF log and an HTML report that I can give to my management. Now the defender details command allows me to dig into these vulnerabilities and I can see what the vulnerability is, where it is in my code, and the severity to help me prioritize. And from here, of course, we're going to fix it. Now using the defender fix command, the system will remediate suggested fixes directly in my local dev environment. And when that's done, I can check out the diff. So, I have full transparency about what the harness has done and I still have a human in the loop check. But of course, everything I've shown you so far has run locally. But I can also create a PR to plug into my existing workflows and push up to my repo. And I can take the SARIF output from the scan and I can upload it to tools like GitHub Advanced Security and manage everything alongside my other application security findings. Now I've shown you MDash working on my code, but let me show you a vulnerability our security research teams identified using MDash that you can go and look up yourself. So the TL;DR, because this is a lot to read, of this bug is that WASM time reads an out-of-date map of an object, runs off the end, and then it crashes the host. So this is exactly the kind of bug that the harness was built for because the flaw is spread across three different parts of the codebase. So no single file looks wrong on its own. They look absolutely fine. And we can even see here, which is probably my favorite part of this bug, a very confident statement from the developers claiming everything is fine. Now, this is exactly the sort of reassurance that fools normal scanners and single AI models. But MDash wasn't fooled. One team of agents spotted the suspicious gap. Another team argued it apart, and a third team built a working example that actually triggered the crash. And it did all of this in an open-source codebase. And this is the kind of joined-up reasoning that previously required significant manual security research effort. This is MDash helping developers create secure code from the start. Coming soon to your CLI and the Microsoft Defender portal. Back to you, Satya.

Sure. Well, hey, I'm sure you guys are wondering what timeline you're on where The Chainsmokers are at Microsoft Build, but hey, how are you? You know, we've been in The Chainsmokers for 14 years. And you know, when our music started to take off in the mid-2010s, we got to play a few events like this. And we met a lot of the founders from the consumer mobile cloud era of startups and they taught us a lot and gave us a front row seat into what early stage investing was like. We fell in love with it. We found a lot of, we had a lot in common with these founders and the way that they built their business was the same way that we thought about starting The Chainsmokers and, you know, kind of breaking through the noise there. And, you know, we got to participate in a few of their deals. We're very lucky and we decided to institutionalize in 2020 and start our own fund.

I mean there's so many vectors you could talk about this on. Of course, there's the creative output side, which, you know, we've been experimenting with in music, but always important to have your authenticity when it comes to creativity. But on the investment side, I think we're moving from producing outputs to producing actions, which I think presents like a very interesting opportunity to kind of reimagine the entire architecture of the way software and enterprise has been built. So instead of humans, you know, producing outputs, it's machines producing outputs and rethinking what that entire space looks like in that context.

Totally. I mean, there really are so many parallels. Maybe it seems like that, maybe it doesn't, but I mean it's really difficult as artists to really find I guess what your sound is, what's authentically you. What you can continue to do over and over because with this much competition, you have to be moving authentically. You have to be connected to the product that you're creating and because you're going to have to iterate and keep it going and have consistency for a long period of time to kind of lock into your fan base and make something that's special and unique. And you know we try to advise our founders to do the same thing.

We'll be there.

Thank you. Thank you. Good morning, everybody. You know, we really are living in the most remarkable times. Since I started working in AI, the compute that we use to train frontier models has increased by 1 trillion-fold. That's 12 orders of magnitude of computation in just 15 years. It's now clear that a consistent exponential increase in computation leads to predictable advances in AI capabilities. And in the next few years, we're going to see three more orders of magnitude of compute applied to train frontier models. Intelligence is now a function of compute. Log-linear hill climbing has become the norm. The scaling laws are clearly holding and it is a remarkable time in our industry. And so in this context, we at MAI are building towards what we call humanist superintelligence, state-of-the-art AI capabilities that are explicitly designed to serve people and organizations and not replace them. Because the type of AI that we create really does matter. We need an AI that places humanity first, that always prioritizes human well-being and human progress. This is the core philosophy and motivation behind our superintelligence efforts at Microsoft and it shapes everything that we do. And as a platform company, our job and our commitment is to keep you developers building at the absolute frontier. So today we are very excited to announce a family of seven new models across image, voice, transcription, and coding. These are all built with real attention to detail and a commitment to making very practical and efficient tools that are tuned to just how you work in the real world. So, first up, MAI Image 2.5 and its flash variant. Two super strong models that deliver a step change in quality. Now at number two on the leaderboard, surpassing the score of Nano Banana 2 on image editing. They give you precise editing with incredible control and consistency. Flash is here for super efficient production workloads while 2.5 gives you that maximum fidelity and professional-grade performance. They're live in PowerPoint today. They're rolling out to OneDrive and right now you can access them on Foundry at a market-leading quality per dollar. Next up we've got MAI Transcribe 1.5. This is the best transcription model in the world. State-of-the-art accuracy across 43 languages, beating out Gemini and OpenAI's flagship transcription models. We've optimized it for real world use so that you can produce highly accurate transcripts for any bespoke use case. Five times faster than all rival models. It's now being integrated inside GitHub, Teams, Copilot, Dynamics 365 Contact Center, and it's now also available in Foundry, where I'm very excited to say it is the fastest, most efficient, and most cost-effective transcription model of any of the hyperscalers out there. So, paired with that, we've got MAI Voice 2. This is our latest speech generation model. It has beautiful, prosody, natural sounding delivery, fine-grained emotional control, and it's available in 15 languages with many more coming soon. We're also announcing Voice 2 Flash, and that provides the very best value and speed for ultra latency-sensitive voice agents, which of course is the big thing in 2026. Next up, our text foundation model, MAI Thinking 1. This is our first reasoning model and it's exceptionally strong in our target use cases of reasoning and SWE tasks. It's a 35 billion active parameter with a 256k context window. That means that it competes in the medium-sized weight class where it's certainly punching above its weight and independent human raters on Surge prefer it in overall quality side-by-sides versus Claude 4.6. It's achieved 95-97% on AIME 2025, which obviously the key measure of its general purpose reasoning abilities. But most importantly of all, it's now at 53% on SWE-bench Pro, which places it right alongside Opus 4.6, at least on the toughest coding benchmark that's out there. So, we're very happy with that. Now there's plenty more for us to do as we get this into production and hill climb against real world tasks and real world traffic. But what is actually most remarkable about this model we think is that it has climbed entirely from the bottom. And that means that it hasn't targeted any of the benchmarks specifically and it's done so with absolutely zero distillation. And to us this is critical because it means that the model is created with an enterprise-grade clean and commercially licensed data lineage. That means that you can put it into production in a very trustworthy way with complete confidence. Now finally I'm incredibly excited to announce MAI Code 1 Flash. This is our new inference efficient coding model which has been especially tuned for VS Code and of course GitHub Copilot CLI. It achieves 51% on SWE-bench Pro despite having just 5 billion parameters. And so it's much closer to Haiku in terms of size, but cheaper in cost, delivering really strong coding performance at great inference efficiency. And it's rolling out today inside of VS Code. Now, alongside distribution on Foundry and optimization for our 1P products, we're also very excited to make our models available on OpenRouter as well as Fireworks and Base10. So, this means that for the first time, you're going to be able to tune the weights directly yourself in an ecosystem of your choice. Now, across this entire family, safety and security have been built in from the start. Our voice models come with protections against unauthorized cloning. Everything is watermarked from scratch. We've reduced our over-refusals, improved representation, including for people with disabilities. And we're also publishing a very detailed technical report today to give you a full and transparent understanding of how we've put all of this together. Now, one of the things I'm particularly excited about is that we have been carefully co-designing our models with our own silicon. So that means that we've optimized MAI Thinking 1 on our very own Maia 200 chip and benchmarked it head-to-head against the GB200. And so on top of the 30% performance improvement that Satya talked about earlier, we're now seeing a further 1.4x performance per watt gain when we run our MAI models on the Maia 200 end to end. And that's huge because as everybody knows at this scale every watt counts and silicon and model co-design is a really key advantage that we think is going to help keep everybody here right on the frontier with the most efficient and most powerful thinking and coding agents out there. We're also super excited that these faster and more efficient MAI models are coming to the N1X that Satya mentioned a few moments ago and we think that's going to be able to deliver the very best performance on Windows in a few months time. Now to us this is what owning the full stack end-to-end looks like. It's the foundation of Microsoft Frontier Tuning lets you customize the MAI models using our full stack hill climbing machine right where you want it. And it means that the disciplined and very relentless engineering that has gone into building our models is now available to all of you on a platform that you can trust working on your behalf to create custom agents that you will control. So the really big thing of course that's happened in the last year is these RLEs, reinforcement learning environments. These unique training gyms for your AIs. They create company and task specific agents adapted only to you built on MAI models. So for example within Microsoft we use our RLEs combined with our MAI models to climb towards the best agentic use cases on Excel. Our MAI tuned model is now on par with GPT-5.4 on public and private benchmarks whilst at the same time being 10 times more efficient on cost. Thank you. You know, and many other early adopters are seeing similar results. When we've tuned our models on McKinsey's tasks, Semi delivered the highest win rate, even outperforming GPT-5.5 and again delivering 10x greater efficiency on cost. So to us this is the advantage of very carefully calibrated Frontier Tuning. And importantly unlike with some of the other companies, with MAI you don't rent intelligence from a shared model that learns from everybody. Only you keep the benefits of your hard-earned workflows, know-how, knowledge, and your own institutional data. Only you get to control the resulting model. And so with us, the RLEs and the models that you build inside of them, they become your moat. I really think this is distinct. It marks a new era in AI that we're all very, very excited about. Okay, so now just one final announcement that I'm very excited about. We are taking customization and co-creation of our models to the highest level possible on what I think of as perhaps the most important application of AI, healthcare. So today we're very proud to be announcing that we're partnering with Mayo Clinic to jointly develop a new frontier model for health and then deploy it around the world in their hospitals and beyond. So this morning, please help me in welcoming to the stage a physician, groundbreaking researcher, president and CEO of the Mayo Clinic, Dr. Gianrico Farrugia.

Well, first of all, thanks for having me here. Thanks to Satya as well. Mayo Clinic is known for being able to live up to our primary value. The needs of the patient come first. We deliver outstanding healthcare. We're ranked the number one healthcare organization in the world. Yet, we know most people in the world will not have access to Mayo Clinic. So 7 years ago we decided to create a platform, the Mayo Clinic Platform, moving all of healthcare from a pipeline to a platform and with our partners that platform now is in four continents and reaches around 100 million people. It has created the largest, to our knowledge, deepest longitudinal healthcare data set in the world, multimodal, including genomics. So here together now we have the opportunity to do what we do best together which is create a frontier model for healthcare. What it means if you're a patient, if you're somebody interested in healthcare, you can get clinical and logistical answers to your healthcare. But if you're a healthcare provider, if you're a physician, it can give you insight. It can act as your real time team member that can tell you what is likely to happen next. But it can also prevent harm and therefore increase patient safety and giving valuable insights that make the team better at giving you what you need most, which is better healthcare.

Yeah. And I think that one of the things that we're most excited about is that the models are already pretty incredible at textbook knowledge. They've read all the journals and all the papers, but what they're really lacking is the kind of clinical practice and clinical expertise of your team and your clinicians that you've developed over the last many decades. So how do you think we might go about using that clinical practice to improve the performance of the model in production?

So the exciting part here is we each do what we do best and we can tackle something that has eluded healthcare for a long time: trusted scalable solutions. And to do that you need to have the right data. You certainly need to have the right people but you also need to have a very patient focused lens. And between the two of us, we now have all this together so we can build this frontier model and then offer safe, secure, trustworthy and of course effective healthcare solutions for all. Well, our number one objective of course is to put the patient first, deliver the highest quality we can in a trusted way, and then hopefully share that with the world over. So, we're very excited for this partnership. Can't wait to share more with everyone in the future.

Us too. Thank you. So today marks some very, very exciting steps that we're taking on our journey to create humanist superintelligence at Microsoft. We now have an incredible roster of seven new world-class models to keep everybody working at the absolute frontier. And we're really looking forward to everybody being able to co-create your own unique agents adapted to you that you'll control. I really feel like this is a new era in AI, an era of AI that you control on your terms. So, let's build it together. Thank you very much, everyone. And now over to Tanya to show us how Frontier Tuning works in practice.

Thanks, Mustafa. With Frontier Tuning, we're making it possible for you to create your own enterprise AI with the models and the harness tuned on your data and your workflows. Let me show you how you can build your own hill climbing machine. MAI Thinking 1 is now available in private preview in the Foundry model catalog. You can go ahead and deploy the model as is or if you want to go on your own hill climbing journey, you can start by clicking the fine-tune button. Now I'm in the fine-tuning UI. The first thing I'll do is add my data set. Next, I'm going to add a grader. And that's it. I go ahead and submit the job. Couple of hours in, I see how the model is learning. It generates rollouts, it scores them, and we start to hill climb. Now, if you want full control over your RL training loop, let me give you a sneak peek into our low-level training API. You can see here I have the MAI Thinking model. I can also configure my rollout strategy as well as my hyperparameters to define exactly how I want my training algorithm to work. In fact, I can also incorporate my own RL gym here by defining the tools that this model interacts with. All right, you just saw all the code, but as an M365 customer, you're never starting from scratch. Let's hop on to Copilot. As a part of Frontier Tuning, we're introducing a new way to build RL environments based on your data and your workflows. One of our customers, Land O'Lakes, one amongst the largest agri-businesses in the United States, is in fact using this to perfect that butter on your morning toast. Let me walk you through their environment. Now on a high level the environment consists of skills, knowledge and tools. But in the back end we create an entire RL gym for your agents to continuously learn the way that you work. Now let's look at the butter report generation skill here. These tasks are very complex. They require many manual steps and a high degree of precision. Now, in these tasks, even 80% accuracy isn't good enough. To help climb to higher accuracy, we're extending the industry definition of skills to include rubrics on what good looks like. But this is just one task. How do I scale this to codify all of the tasks in an enterprise? You spend a lot of time in M365, in Teams, Outlook, Word, Excel, and PowerPoint. We use those signals to suggest skills and rubrics that truly define the way that you work. Next, you can add your organizational knowledge for grounding like from OneDrive, SharePoint. Now, the environment comes built in with Microsoft tools and you can add custom tools to it. Because these tools tap into real workflows, we virtualize them to simulate execution. So the model can learn without actually impacting the live state of your business. And now my most favorite part, the science. By generalizing all of these learnings into the main model as well as in the embedding model, we're able to hill climb for tasks that require high accuracy. And not only this, we are able to hill climb to more than 90% accuracy for Land O'Lakes tasks using the MAI model. In fact, we estimate this model to be 10x more efficient than the baseline models. Now, with this environment set up, let's go back to butter reporting. Let's run this task this time using this tuned environment as an inferencing harness. Now, usually this task takes a couple of minutes to run. In the meantime, I will show you a cached response. Now, as you can see here, the agent has leveraged test time compute with multiple models, including a fine-tuned model. And what you see here is a summary that doesn't feel generic. It feels undoubtedly Land O'Lakes. And that's not it. The task holds itself to high standards and it continuously retrospects and evaluates itself. This way with Frontier Tuning, your agent continuously improves with your compliant RL environment on your data encoding the way you work. Now that is what I call Frontier Tuning as smooth as butter. We cannot wait to see the environments you build. Back to you, Satya.

Hello. I work in the Microsoft Discovery and Quantum team. Today, to recycle a PET plastic like this bottle, you have to shred it and melt it. The result is degraded. You cannot use it to make this bottle again. It's downcycling. Cambridge Consultants, part of Capgemini, is using Microsoft Discovery to advance that research. Let me show it to you. This is the Microsoft Discovery app. It is based on VS Code because Agentic Discovery has many parallels with Agentic software engineering. Today I want to do these three things as a scientist. First, I want to write a scientific paper about this topic. I'll explore an existing line of research. Instead of melting the plastic, I can use proteins to decompose it. So, you can recycle it again and again. Second, I want to perform the actual discovery to find new proteins. And third, I want to create a lab protocol to test the results in a real lab. If you are a developer, these three steps should be very familiar to you. They look a lot like planning, coding, and testing, and deploying to production, but for science. Let's launch it. This will kick off the discovery engine. You can think of this as a team of specialized agents always running in the background following the scientific method. You can see those agents here and you can add more. Microsoft Discovery includes a community of agents, models, and tools across many domains. You can use open source, third parties or create your own. Okay, so this is still running and it will be running for a while even hours or days because the discovery engine like science is not sequential. It is exploring hypothesis and performing long running simulations dynamically. So let me open one that is already completed. You can see the files that it created here. This one is the research paper that I was asking for to create it. Discover is using a knowledge graph internally bringing together public scientific literature and internal knowledge. This is a critical asset because it provides complete visibility of the reasoning so scientists can be in full control. We can go now to step two, the discovery itself. Let me open the output for that one. So how do you come up with new proteins to decompose plastic? This is the approach the discovery took. First, you need an AI model to predict how good a protein is for that. Then you need a way to generate new proteins. And finally, you need to identify the most promising ones. So, let's start with the model. Microsoft Discovery trained multiple models and picked the best. And there's no out-of-the-box agent for that. But that's okay. If Microsoft Discovery does not find an agent for a task, it will create one on the fly, which is pretty cool. So, here's all the files that it created. This one here is the YAML for the agent. And this one, for example, is the Python code to train the models. Now, we need to generate candidates for new proteins. And that task requires a lot of compute. Discovery is integrated with HPC so agents can use it for complex simulations. You can see the process here. It started with a seed protein and then it created variations by replacing small segments. You can see those segments there. Then it applied the model from before to see the variation helps or not learning in that process. This is done millions of times in multiple jobs in parallel exploring a huge tree of proteins. The result is 80 proteins that are ready to be sent to the lab for testing which we said it was like deploying software, right? The problem though is that creating a protein is a little bit more complicated. The most common way is inserting DNA into bacteria. So the bacteria create the protein for me. So Discovery created another file. This one here. This file contains all the DNA sequences to create each protein. I can send this now to a lab, create the proteins and test them. Or I can go one step further. If I have an automated lab, I can integrate it directly with the agents. Let's do it. So let me go back to the session and let's just submit job to lab. Go. This will use a custom agent that is sending the instructions for the lab equipment and this is very real. Cambridge Consultants does have an automated lab and I have it right here. So this is the application control in the lab. It has a Copilot interface. So the scientist can interact with the lab to design experiments which is, it feels like being Iron Man but for chemistry. In this case discovery is submitting the job. You can see it already here. But remember you need to grow the bacteria. So this will take some time. So let me open a previous run. And there you go. These are all the steps that the agents are doing. Most of them completely automated with human supervision. How cool is that? This is bringing together the physical world and the digital agents in a unified discovery loop. And this was just one example of what Microsoft Discovery can do. Customers across industries are using it today to embrace a new era of scientific discovery. Thank you. Back to you.

This is one of those rare moments when something that once felt beyond imagination suddenly feels within reach. Quantum is our most fundamental physical theory. It's really the low-level operating system of the real physical world. For quantum computing to deliver real world impact, it has to be both reliable and scalable. Reaching quantum at scale will allow us to take a more direct approach to solving problems in chemistry, the discovery of new material, life sciences, the way in which we bring drugs to market. It opens up millions of possibilities. Quantum will change the world. And this change is just beginning to happen.