Models, runtime, and silicon, all working together to create a meaningful experience. We're working with the GitHub team to bring our vision for hybrid intelligence to life on Windows. Earlier this year, GitHub launched Hydra Fusion, which routes each task to the right model in the cloud. Today, we're extending intelligence to Windows, letting Hydra Fusion tap into models running locally.
Now let's talk about these models. At Build, we introduced MAI-1 and Flash, a 130 billion parameter coding model built for real-world developer workloads. Now we're bringing that same intelligence to your device. This is where full-stack engineering comes in. We have taken that model to three bits of precision, reducing its size by nearly 80% while preserving coding quality. That is an incredible feat of engineering. But really, shrinking the model to the device is only half the story. MAI Code can work at an impressive 256K context window right on your device.
Now, our goal is simple: let AI run locally, stay responsive, while leaving room for everything else on your PC. And there are many more powerful models coming to Windows. NVIDIA is working on a new Neatron model. At over 70 billion parameters, it manages to quantize to 2-bit and uses just over 20 gigs of memory, making it ideal to run on RTX Spark.
And why stop there? With advanced quantization methods, it's possible to bring more of the frontier to your PC. At 1.66 bits, DeepSeek Flash V4, a 284 billion parameter model, is running locally on device, fitting in just — that's right, it's quite amazing — fitting in just 60 gigs of memory. This model outperforms GPT-5 coding and reasoning benchmarks. A year ago, such capabilities were exclusive to frontier cloud models. Now you can have these capabilities running on your PC.
And this is just the beginning. To power this growing ecosystem of highly capable models, you need a performant runtime. And this is where Windows ML comes in. Windows ML is a scalable runtime to deploy models across GPUs, NPUs, and CPUs. And we're continuously evolving it. We're excited to announce we're bringing Llama.cpp to Windows ML.
This gives developers more choice: open-source models on day one, the ability to experiment quickly with emerging models as they land. With intelligent routing, highly capable models, and a performant runtime, a clear recipe emerges for what hybrid intelligence looks like. That's the repeatable pattern we'll apply to light up experiences across coding, security, creation, and work. Kayla is here from the GitHub team to show you this in action. Come on over, Kayla.
Thank you, Pavan. I'm excited to show you how I'm going to use the best of cloud and local models to do my coding workflows in GitHub Copilot on Windows. So, to start, let's just paste in a simple prompt. And I have the Windows Terminal codebase cloned onto my machine. So what I'm asking is I want our agent to detect unused code, imports, packages, and obsolete feature flags, and then give me an actionable summary. So this is just basic code cleanup that I'd like our agent to do.
And then here at the bottom, you might notice in the model picker, I have auto efficiency enabled. So this is going to select my model depending on the complexity of the task. And now with our new version of auto, it will automatically pick a local model depending on if that is a more simple task. So here we could see on the left-hand side our GPU has ticked up, and that's because we're leveraging our local model here. And I did run this same prompt before on a different codebase, and we could see that it leveraged MAI Code 1.1 Flash local.
Now, since my code is cloned to my desktop and my model is local, we could just put our whole machine into airplane mode, and this process will still continue to turn because everything is right here on the device. So this is just a simple coding task right to the local model. But what if we asked something a little bit more complex? So in this session, I asked our agent to look at the issues in the Windows Terminal repo that have the needs-repro labels. So these are issues that our team hasn't been able to reproduce.
And then I wanted to do some investigation and then hand off to some local sub-agents to do further building and testing and see if we can get more information here. So this prompt actually ended up leveraging GPT-6 Luna. So this is a cloud session here. And then we've got three local sub-sessions happening on the left-hand side leveraging MAI Code 1.1.1 Flash local. So this is a really great example of hybrid intelligence.
If I hop into one of these local sessions, we can also see all of the tokens that I got for free. And on this one in particular, we've got 1.66 million being sent to the model and then 10 and a half thousand coming back from the model. And all of this was free, and this was just one of the sub-sessions that I ran. So I have a lot of sessions going. I like to take a look at what I've got running on my machine and what I've run before.
So we have a canvas that we created that is a really great visualizer of my sessions. So a canvas is a custom UI that works inside the Copilot app where both you and your agent can work back and forth and update the UI accordingly. So here in our visualizer, all of my purple sessions are local sessions that I've run with our little leaf, and all of my blue sessions with the cloud are cloud sessions that I've run. And you might notice I've got the same session happening a few times.
I'm doing issue triage a lot, and I'm doing it locally. And that's because issue triage is one of the tasks that takes a while, and it's something that we have to do pretty frequently. So I've actually set this up as an automation. So automations can run on a cadence or on a trigger. And I've set my issue triage one to leverage the local model explicitly. So I know for a fact that it's going to use just the model on my machine, and it's not going to spend any credits without me knowing.
So I can have this running overnight, and I'm still getting all of those tokens for free even while I'm asleep. So these are just a few scenarios that I've showed today. Imagine how much more you can save doing these workflows always on, even when you step away. Thank you so much. I'm going to hand it back to Pavan.
Thank you, Kayla. That was awesome. Hybrid intelligence Windows powered by Hydra Fusion will be coming to the GitHub Copilot app, CLI, and VS Code starting October 15th. You've seen how Windows is the most secure platform for hybrid intelligence. Now, to make AI a part of everyday life, we need to bring it to places people use the most, into the flow of human ambition.
We're moving toward a future where everyone can simply express what they want, and their Windows PC, with their permission, can take action on their behalf. For hundreds of millions of people each day, search on the taskbar is where intent begins. So, it's only natural our story starts there. At its core, the new Windows search experience is quicker, more efficient, so you can find your apps, settings, and files faster.
Starting this fall, on Windows 11 PCs, you can take thousands of quick actions directly from search on the taskbar. You can switch to dark mode. You can turn on do not disturb to help you stay focused. You can turn up your mic volume and then use the inline slider to get to the exact level you want. All of this as fast as you can type.
And actions can do more than just settings. You can take a screenshot directly, arrange your windows, all from the taskbar. And my personal favorite, you can send a text message to a friend or colleague just letting them know you're running a little late without having to pull your phone out of your pocket. A quick, intuitive way to move from intent to action.
And of course, right from the taskbar, you'll be able to connect to the new Copilot app when your request calls for it. Ask a quick question, and Copilot will bring the answer right there to you in line in search. If you want to have a richer conversation, you can jump directly from search to the full Copilot app. To tell you more about the Copilot experience on Windows, please welcome Jacob.
Thanks, Pavan. Recently we introduced the new Copilot with three core experiences: Home, Code, and Autopilot. Today I'm really excited to talk to you about how Windows hybrid intelligence supercharges everything that we've just brought to market. First, let's turn it on. Copilot now has access to three new things. First, local context. Local context means that Copilot can now ground over all of the files on your PC the exact same way that Work IQ helps businesses ground over all of their business context in the cloud.
Second, local actions. Local actions means that Copilot can actually take action directly on my machine. It can move files. It can change a setting. It has the same level of the ability to control and change things that I do as a user, but always with permission. Third, local models. Copilot will still use the cloud for the hardest tasks, but for times when cost or privacy matter more, it can delegate down to local models that run directly on your computer.
So, let's take a look at how this transforms the rest of Copilot. Let's first talk about Home. Home is the new starting point for Copilot. We started by bringing together chat and coworker. We also introduced Office in Copilot, which brings full, rich document editing and collaboration from Office directly into the Copilot app. And today, Home gets even better with hybrid intelligence. Home now knows what's on my PC. And so the exact same local files that I've been working on locally myself can now be turned into collaboration-ready artifacts directly inside of Copilot.
Next, let's talk about Code. Code is the easiest way for anyone to build and share an app. No developer experience required. Hybrid intelligence makes Code better in three key ways. First, you can now build a native Windows app from a single prompt. This means unlimited apps and widgets, all native on Windows, directly from the Copilot app. Building software for Windows has never been this easy.
Next, we introduced MXC support. MXC support means that your code, even locally, is running in the sandbox that we heard about earlier. This means for the first time, not just developers, but anyone can write and run code on their local machine with confidence. And lastly, local models. Copilot is now able to hand work down to a local model as it's coding. This means that projects move faster and cost less, just like we saw in GitHub Copilot.
These three things turn your PC into your personal software factory. We really can't wait to see what you build. Lastly, I want to talk about Autopilot. Autopilot is an enterprise-grade, long-running autonomous agent. Let's see how hybrid intelligence makes Autopilot even better. We'll flip over to a quick demo.
So, over the last couple weeks — maybe a little bit late — I was working with Autopilot to pull together a bunch of stuff to file my taxes. And so, I'll go ahead and just kick off this simple prompt. And the first thing that Autopilot will do is it'll go into my inbox and it'll find the email from my accountant and it'll look for the specific list of files that we have to pull together.
Now, because of hybrid intelligence, Autopilot can now ground against my entire PC. And the documents I need to file for my taxes are all over the place. They're in Downloads. They're in Documents. Half are organized, half aren't. That doesn't matter. Autopilot's able to use hybrid intelligence to ground over absolutely everything with local context. With my permission, it starts to organize. It moves everything into a new folder, pulls things together, and actually starts to rename files.
Now, all of this organization and renaming is actually happening with local models. My tax details are not going to the cloud. It creates some subfolders here to give a little bit more structure, and then it finishes by zipping everything that it found into a single zip file that I can easily send to my accountant. Now, Autopilot of course always goes one step further, and it also actually goes ahead and composes an email for me to send directly back to my accountant with the zip file that I just created attached.
But of course, it pauses here and it makes sure that I'm good to go. I review everything. This looks great. We can go ahead and send the email. And just like that, all of these files are now sent off and in my accountant's hands, and we're good to go.
So that's Windows hybrid. Copilot now truly understands your PC. It's able to take action on your computer. It's able to keep sensitive work on your device. And at every single step, you're in full control. Really, it's Copilot built natively for Windows. Copilot features that are powered by hybrid intelligence are coming to Copilot over the next couple months. And with that, I'll hand things back to Pavan.
Thank you, Jacob. We're excited to bring hybrid intelligence to Copilot. Windows is the largest open end-user computing platform. So along with great Microsoft experiences, our broader ecosystem is also taking advantage of Windows platform capabilities. We're seeing popular agents like Hermes, OpenClaw, and Perplexity building local AI experiences on Windows.
Here's an example from Perplexity. Let's say you're an aspiring photographer trying to get off the ground, and you have a folder of photos from a recent photo shoot that you want to turn into a professional gallery for your client. Normally, that means you'd be paying for a service, resizing images, building a page, bringing it all together. That takes multiple steps and multiple tools.
With Perplexity's portable computer on Windows, you can just describe what you want: resize the photos and build a local gallery. Perplexity takes it from there, finding the photos, checking the tools on my PC, and building the gallery. You define the outcome, and Perplexity figures out the next steps. The model is running locally on the PC. Processing stays on the device without using Perplexity computer credits. And here it is: 12 photos in a browsable gallery, complete with titles and image details.
We're also excited to announce Muse is coming to Windows as a native app with MXE integration.
To achieve our vision of bringing unmetered intelligence to every desk in every home, you need a broad range of PCs and silicon, from a mini desktop PC to a commercial laptop to a supercomputer. Let's start with the mini desktop PCs, or claw boxes as people call them, which millions of people use today. It's a natural home for a device and an agent that needs an always-on device, an agent that keeps working even when you step away.
Today, we're excited to announce a new Get Started experience in Windows. I'm excited for this because it's something customers are asking for: a simpler, more secure way to get started running agents. This experience makes setting up popular agents fast, just a few clicks right out of the box. No terminal, no complicated configuration. And for those of you using OpenClaw, we're introducing a native Windows gateway with a simpler setup.
And because all the popular agents are adopting MXC, you can now get these agents running securely on Windows. This is just the beginning. As we continue to learn from our customers, our ambition is to bring secure agentic experiences to the billion people who use Windows every day.
Next, Copilot Plus PCs, your PC for everyday AI. We introduced these thin, light, powerful PCs two years ago with power-efficient NPUs that run everyday tasks locally. Today, over 40% of the laptops being built for business are Copilot Plus PCs, tens of millions of PCs a year. And we're seeing over two trillion inferences per month running locally, improving customers' experiences across search, imaging, and video calls. The hybrid intelligence experiences you just saw from Jacob will take advantage of local AI on Copilot Plus PCs.
And then there's the builder tier. To create a new class of devices for builders, we needed to understand what matters to them and push the boundaries of hardware engineering. I want to introduce you to Surface Laptop Ultra, the most powerful Surface we've ever built. Let's take a look.
This is Surface Laptop Ultra. We built it for developers and creators pushing the limits of performance who want portability with exceptional craft. I've been using Surface Laptop Ultra for the past few months, and it's an absolute game-changer.
Developers push their machines hard. Here's an intensive developer workflow with GitHub Copilot. But a demanding task shouldn't mean putting everything else on hold. While that work continues, a film is being mastered in DaVinci Resolve. This kind of mastering takes serious performance. The CPU, the GPU, and memory all working together while the developer workload is still running. And Gears of War, which released yesterday, has been running on this machine the entire time. And it runs awesome.
It runs simply awesome at 60 frames a second. Work, create, and play, so you don't have to choose just one. Now, it wouldn't be Windows and NVIDIA without talking a little bit more about gaming. At launch, hundreds of AAA titles that you love and the indies that you play will be great on RTX Spark. And we're super excited to announce that in 2027, Call of Duty will be coming to RTX Spark.
Getting here took Surface, Windows, and the NVIDIA team building a new platform from the ground up. When three teams come together, you get something none of us could have built alone. And for that, I am deeply grateful. We built Surface Laptop Ultra around NVIDIA RTX Spark. It took end-to-end engineering, design, and full-stack thinking to unlock what local AI can do.
With up to 128 gigs of unified memory and up to a petaflop of AI compute, you can run models on this laptop that simply don't fit on a traditional machine. Surface Laptop Ultra is engineered beautifully inside and out. It has more than two and a half times the thermal capacity of our existing laptops. So, it stays faster even under sustained load.
And you don't have to worry about sacrificing performance when you're unplugged on battery. It can maintain over 99% of its power while on battery power. And that's great because it's portable, under 18 mm thin, and less than 4.12 lb. I also love our new PixelSense Ultra Touch display. It has the brightest peak HDR of any laptop. It looks incredible.
Our Surface Laptop Ultra has the most ports on a Surface Laptop. Our customers have told us they love magnetic charging and they love the versatility of USB-C. So, we invented Magnetic Connect, the world's first built-in magnetic USB-C charging. I know. Just bring the connector close, and it snaps right into place. Easy magnetic charging without giving up the USB-C port.
But the real measure of all of this power is what happens when it's in your hands. When you can close the distance between what you can imagine and what you can create, that is amazing. That matters because the future of AI is not about having just one model. It's about having the right model at the right moment. Frontier capability when you need it, smaller, faster models when you want speed, responsiveness, and the room to keep the rest of your workflow running. Surface Laptop Ultra is built for that future.
It brings new ideas to life with advanced graphics, unified memory, and local AI working together. Let me show you how one of our designers put all of that power to work, combining local and cloud AI to bring his creative vision to life.
Hi, I'm Danilo, and I'm a senior designer at Microsoft. In my day-to-day work, I'm building products, but outside of work, I have a true passion for creating concept art. I love creating concept vehicles in sci-fi worlds, and I'm about to show you a workflow that used to take me days and now just takes me a few hours.
One of my favorite tools is Blender. Here I'm building a spaceship from scratch, modeling all the details, texturing, lighting, and rendering. I could keep going, but if I wanted to bring more of my story to life and create an entire world around the spaceship, it would take me days or even weeks. That's where local AI can be really helpful. It lets me explore ideas faster.
Here I'm using ComfyUI, an app that lets me download and run AI models locally. I started by importing some of my renders as reference images and used Gemma 4, an image-to-text model, to generate a description of the world and a list of assets that match the style and elements of my images. Then I imported another image with close-up details as reference, along with asset descriptions generated by Gemma 4.
Using Flux 2D Dev, an image-to-image generation model, I create an image of each asset. After running several generations, I end up with a folder full of assets. Next, I imported my favorite images into Hunyuan 3D 2.1, an image-to-3D model, to generate the 3D assets. I then brought all these assets into Blender so I can start building my scene.
Using a highly capable coding model running fully on my device, I wrote a basic prompt and generated several layout options using my assets. After choosing the one I liked, I kept exploring and refining my scene, adding more elements and tweaking the overall composition.
Once I was happy with it, I rendered the scene, brought the images into Photoshop, and painted over areas I wanted to refine, adding elements like smoke and adjusting the overall color and mood. Then I took the finished image back into ComfyUI and used Wan 2.2, an image-to-video model, to generate the final animation.
In my final step, I used GPT-6 Astra in the cloud to take things even further. I uploaded my final files and asked the agent to build a scene in Unreal Engine that I could actually walk and fly around in. So this is the result. This would normally take me days, but now only takes hours. All on my Surface Laptop Ultra.
What you just saw is Danilo using multiple local and cloud models in a variety of different apps, bringing his creative vision to life, shaping the design, refining the details, staying in control of his entire workflow. What used to take Danilo days now takes hours. That's what we mean by hybrid intelligence.
The new Surface Laptop Ultra is built for this new era of creative work. Across the models we tested, Surface Laptop Ultra delivers faster time-to-first-token performance for language models and shorter image generation time than M5 Pro-powered laptops. This is where having both the large memory and the petaflop of AI compute on a single device is a distinct advantage. For builders using local AI, that means less waiting and more creating on Windows.
Since Build, many of you asked about the Surface RTX Spark Dev Box, built for those of you who want to push local AI development even further. It ships with Project Zenith, a ready-to-code Windows experience preconfigured out of the box so you can build from day one. I'm excited to share that both Surface Laptop Ultra and the Dev Box are available today for pre-order.
In addition to Surface, we've built a broad device ecosystem. NVIDIA has been our close partner on this journey. RTX Spark brings powerful AI compute to the laptop. DGX Station takes it even further. Look at the ecosystem bringing this to life.
Lenovo Yoga 9 2-in-1, a lightweight convertible design with pen support. ASUS ProArt P14, an incredibly thin design with a 4K OLED display. Dell XPS 16, a seamless glass haptic touchpad with integrated speakers. MSI Prestige N16, a versatile flip form factor with pen and UHD+ OLED display. HP OmniBook Ultra 16 with a distinctive tower hinge design and gesture controls on the haptic touchpad. And of course, the Surface Laptop Ultra.
All of these RTX Spark laptops are available for pre-order starting today and will begin shipping next Friday.
And finally, we have the supercomputer. DGX Station for Windows brings data-center-class performance together with enterprise-grade security and manageability. This unlocks the power to run frontier-class models locally, from Llama 4 Maverick to Kimi K2.6 to DeepSeek V4 Pro at more than a trillion parameters. Capabilities that once required renting...
A cluster now in a deskside supercomputer. With RTX Spark, we're bringing last year's Frontier to your laptop. With DGX Station, we're bringing last quarter's Frontier to your desk side. Take a look at this.
Take a look at this. For the first time, DGX Station running Windows Live with Dell Precision Pro. Frontier models and agents work locally, taking months of work, complex work into moments with more control over sensitive data and cost. With the HP ZGX Fury AI station, it shows what happens can work and scale simultaneously across 32 agents, a virtual token factory for development teams. Both coming later this year, running the Windows platform organizations know and trust.
This is an amazing moment. On behalf of Microsoft, I want to say thank you to all of our partners. Next, Satya, Jensen, and Shrian Krishna are going to take the stage in a few moments to share their thoughts on the opportunity ahead. Thank you.
All right. All right. Wait, wait, wait. Take the phone. No phone calls on stage here. Um, first off, was that cool or what? What do you guys think? Huh? Yeah.
Huge congratulations to Pavan, Jacob, the teams at Microsoft and Nvidia. Um, I will say they were kind enough to send me a Surface Laptop Ultra for the last few days. It is amazing, but I think I want the workstation for Christmas. Now, now there's a bunch of stuff about the exciting future to get into. But before we do that, I was looking both of you up last night on YouTube, and it's quite rare to see both of these gentlemen on stage together, and I was thinking about how, you know, you both had a history, these two great companies had a history going back decades. You both individually had histories of working with each other going back decades. Ties to me. My first job was at Microsoft. My first paycheck a GeForce FX to play Half-Life 2. So, so maybe to start us off, Jensen, what is your favorite memory or fun memory of working with Satya and Microsoft?
Well, Nvidia was founded because of Windows. It was 1992, late '92, Windows 3.1. And we were just starting to see the vision of what a personal computer really can be. And at the time, frankly, we had never, I'd never even seen a PC before. I largely worked on workstations. We had, you know, we had file servers and supercomputers that we used to design chips with. And the founders and I, you know, talked about this future where maybe a personal computer with 3D graphics would enable it to do things like games and be a workstation and design things. And so we imagined about this world. If not for Windows, Nvidia would not have been founded. And then, and of course, we started in 1993. Windows 95 really reinvented Windows 95.
Wow. Am I on? Windows 95 made possible the GPU to be connected to a personal computer. And what revolutionized the GPU was DirectX. Direct3D was the API that really enabled us to reveal the capabilities of GPUs into applications. But then DX8 came along and we co-invented with Microsoft the world's first programmable GPU, which is programmable shaders, which a few steps later evolved into CUDA as we know it.
And so if you look at the entire journey of our company, Windows was at the core of it. And if not for Windows, there would be no GeForce. If no GeForce, CUDA wouldn't come to the world. Without CUDA, Alex Krizhevsky and Ilya Sutskever, Yann LeCun, you know, Andrew Ng, none of them would have found a computer to do deep learning.
And here we are. And Jensen is now going to do a little transfer of market cap back to me.
I do that every day. I do that every day.
But it's extraordinary that journey that Jensen recounted. I mean, it's kind of the history of computing in some sense. And the thing, one of the things that Jensen sort of sparked for me was he has been perhaps the most consistent with the vision of what accelerated computing can be. And this going back to the trajectory, you've been the one person in our industry who has had this one idea whose sort of trajectory has been the most expansive, quite frankly, more so than even any single PC form factor or chip architecture. Because I look back, one of my first memories was we were talking about essentially saying in the cloud, how big can be accelerated computing or high performance computing, right? The first deal I remember was in Azure, how do we make HPC a bigger thing? And by the way, if...
Satya was the first user of InfiniBand inside a cloud, which brought the first supercomputer to OpenAI, which boom.
That's right. If we had not done that, like the fascinating, you know, like life is full of accidents, but if we had not done that for HPC, we wouldn't have had the supercomputer to give to OpenAI to go train a GPT model, and the rest would have been history. I mean, it was kind of one of those happenstance that we had done the HPC stuff when we were building an InfiniBand-based supercomputer, and comes Sam and says, "Hey, do you have computing?" And we said, "Yeah, maybe." And so it's sort of like, but the thing that I really give Jensen all the credit, quite frankly, is that consistency of vision of what this can be, not today, not tomorrow, but as a secular trend. And that's kind of what, you know, brings us even to this moment.
And then it led to a conversation. It led to a conversation I remember quite well where Satya and I were talking about, so what does this mean to the PC? How do we make the perfect PC in the post era of all of this stuff? And, you know, of course, the personal computer is the ultimate tool. It's my ultimate tool, and for a whole generation of people, it's our ultimate tool. What happens in the era of agents, when the agent is on your computer, it is now your personal assistant, but it wants to have access to all of the amazing tools that we have, but the amazing tools are spread out in the NVIDIA ecosystem into gaming PCs, into workstations. And that's where most of the tools were, right? All the DCC tools and all the Siemens tools and Autodesk and Adobe, and you saw many of them just now. And, you know, we wanted the world's best Blender, the world's best Unreal, the world's best Omniverse, the world's best Catia, everything all sitting on the same PC as the AI. And so how do we go create that? By the way, took four years. Wow. That was a four-year-ago conversation, and just incredible.
Now that's very a good segue today, and Satya. So I think the last few months, you know, we've been sort of inundated with these stories of agents and security. You know, we all use agents. I'm sure everybody here is using a Muse or Instinct, and you are giving full disk access, and you're having it act as you. And today when you guys showed MXC, it seemed to me like a fundamental new operating system primitive for agents, which is a break from where we have been. Maybe talk to us about why the OS is the right place for this and what this primitive, if you will, opens up.
Yeah. No, first of all, remember this all started with coding agents all running on the desktop. I mean, in fact, if you sort of look at where is the agent that needs this security primitive the most? It's the local harness and its access to the file system and to be able to take action. And so that's why, in fact, you know, working with Jensen and team, we said, look, how do we wire this into the primitives, whether it's in the cloud or in the operating system locally? And given the fact that essentially coding went there first, where the harness is local, even if you were using a cloud model, we needed to make the desktop the most secure place for agents to execute. And so that's where MXC comes from. It's fantastic to see OpenShell now fully integrated and native with MXC. The controls that Pavan talked about, right, which is containment itself has to be done at every level, right? It's not about sort of doing one thing and leaving the other, right? It has to be at the microVM, it has to be at the VM, it has to be at the session. The piece that I'm most excited about is the Windows session having containment, right? It's so critical. Then, okay, containment is wonderful, but then I need full observability. How do you get observability? You get it if you have an identity for an agent that I can track. I can then see the entire exhaust of the agent, right? All of it. And then I can govern it. That means I can, like classic, set policy and have it govern. That is now what's going to give peace of mind, whether it's to IT ops, to SecOps. And by the way, and the other thing that we should also not forget is the ability to track the token spend, right? And where it's happening. And so that's the entirety of what we have tried to do together. It's not about any one thing. It's about stitching all these things. Having seen all these adoption cycles, whether it's the consumer adoption cycle, the enterprise adoption cycle, I would say the thing that blocks broad diffusion is not having that complete thought on what is required. Right? So if you do one and leave the other, there's no trust and what have you. So I feel very, very good that we have brought it together in the cloud, in the client, across it, SACK and FIN, and it's heterogeneous, right? It's not about any one piece of Microsoft technology. It's about supporting it all.
