[00:50] >> Good morning.
[00:51] It is fantastic to be back in
[00:54] San Francisco build.
[00:54] It is always fun to be a
[00:56] developer conference to see
[00:59] great change.
[01:00] Developer conferences are always
[01:02] about understanding tech shifts
[01:04] and understanding the new stack.
[01:07] It is also about really coming
[01:09] to grips with the new
[01:10] opportunity for us as
[01:12] developers, for the companies we
[01:14] work at as well as the broader
[01:18] world.
[01:18] Today we are going to unpack
[01:20] this.
[01:20] This conference is all about
[01:21] that.
[01:22] If there is one key take away it
[01:24] would be this, how do you all
[01:27] participate fully in this
[01:30] frontier intelligence ecosystem.
[01:34] It is not about any one piece of
[01:36] technology you hear about or
[01:37] even the platform itself, it is
[01:39] about the value that you can
[01:41] build, you can compound, you can
[01:43] create on top of the platform.
[01:50] That's what the conference will
[01:52] be about and that's what we will
[01:54] focus on.
[01:54] Let's take a look at this AI
[01:56] stack this conference is going
[01:57] to unpack in a great amount of
[02:03] detail.
[02:03] It starts with ubiquitous
[02:06] compute fabric that expands the
[02:08] edge and the cloud.
[02:10] You then have this layer which
[02:11] is the emerging layer where you
[02:13] have the models, you have
[02:15] context, you have the tools, the
[02:17] models can access, and then you
[02:22] have the run time where you
[02:25] deploy the agents and
[02:26] applications that you build on
[02:28] top of the content player and
[02:30] model layer.
[02:31] Of course you have the best
[02:34] tooling to do all of this and
[02:36] you have the security and
[02:37] compliance and governance.
[02:43] That's the simplest form.
[02:46] Let's start where it all starts
[02:48] which is infrastructure.
[02:50] In fact let's start at the edge
[02:52] with windows.
[02:55] The amount of compute there is,
[02:57] at the edge is actually
[03:00] astounding.
[03:01] Think about every NPU, GPU, CPU
[03:05] even, every PC
[03:11] we ask ourselves one simple
[03:25] question.
[03:29] Can we do that with this era of
[03:30] AI.
[03:31] In some sense we are delivering
[03:32] that already.
[03:33] When you look at something like
[03:35] outlook summarized it is using
[03:37] on board AI locally.
[03:39] Same thing with PowerPoint all
[03:41] text.
[03:41] Or teams super resolution.
[03:44] It is just not Microsoft.
[03:47] It is Adobe effects or premier.
[03:51] Using windows ML in order to
[03:59] happen to all of this compute
[04:01] power is to expand the scope of
[04:05] windows ML and windows AI.
[04:09] You now are full install base of
[04:12] GPUs you can get to.
[04:13] I am thrilled every developer
[04:14] out there can count on building
[04:17] for local on board AI and then
[04:18] have it run across all of the
[04:22] install base.
[04:24] Now if you are -- we are also
[04:27] announcing two cool models that
[04:29] are all going to run on windows
[04:33] in INVOX.
[04:39] Eye on instruct, it is a great
[04:43] reasoning model.
[04:44] We have the planning model eye
[04:49] -- Ion plan.
[04:51] You have a full local agentic
[04:53] loop you can give it tools
[04:55] access and build a fully agentic
[04:59] applications without having to
[05:02] run trip to the cloud.
[05:05] In order to really push the
[05:07] limits, realize this unmetered
[05:11] intelligence, there is a lot of
[05:13] hardware that we are adding too.
[05:15] We are thrilled to see the
[05:19] innovation through the
[05:21] ecosystem.
[05:21] The stuff from Intel is exciting
[05:23] to see.
[05:24] Qualcomm announced two sets of
[05:26] things which are great one on
[05:27] the high end snap dragon X on
[05:31] the lower end to sub 500PCs.
[05:39] Of course that brings us to inn
[05:44] NVIDIA.
[05:44] This is next generation SOC for
[05:48] PCs.
[05:48] It brings together the CPU the
[05:51] GPU as well as AI capabilities
[05:53] into a single SOC.
[05:55] It includes unified memory,
[05:58] architecture and also integrated
[06:06] DRTM.
[06:06] You have the SOC coming
[06:13] together.
[06:14] One we build is the Surface
[06:17] Ultra it is a beautiful device.
[06:19] It brings the power of inn
[06:22] NVIDIA together with the design
[06:23] and Craftsman ship of Surface,
[06:27] 128 giga bites of unified
[06:29] memory.
[06:30] Beautiful 2,000 display, and it
[06:33] has an all day battery life.
[06:41] Excited to see this later in the
[06:42] fall.
[06:42] It is wonderful to see all of
[06:44] the designs from all of the OEM
[06:47] partners building and taking
[06:49] advantage of the SOC and the new
[06:51] platform and bringing out pretty
[06:54] exciting machines that are going
[06:55] to be all available come this
[06:58] fall.
[06:58] So, of course, we said, okay,
[07:00] this is fantastic.
[07:02] What can we do next?
[07:04] We said, let's try and push
[07:07] this.
[07:07] Push the architecture to its
[07:10] limit for developers.
[07:12] What if we could just Max the
[07:15] compute, Max the memory, build
[07:18] out that developer machine that
[07:20] is the dream machine, and that's
[07:23] what we are announcing today.
[07:25] Surface RTX, dev VOX.
[07:31] Let's roll the video.
[07:43] [Music]
[08:35] >> It is truly a dream machine.
[08:37] It has 1 Peta flop of AI
[08:40] compute.
[08:42] 20CPU cores.
[08:43] All of those things have 128
[08:46] giga bites of unified memory
[08:49] access.
[08:49] So super excited.
[08:54] I am on the wait list as well,
[08:57] there's a wait list.
[08:58] We will get there.
[08:59] [Laughter]
[09:00] So then we said why stop there?
[09:05] We said what if we did one more
[09:08] thing?
[09:09] In fact I think Jensen did that
[09:12] already.
[09:13] He said windows is coming to the
[09:17] DGX station.
[09:17] I describe it as the desktop
[09:20] data center which you can have
[09:24] running a 1 trillion model
[09:29] locally.
[09:29] It is close to what we perhaps
[09:31] had when we built GPT-5 or 3 one
[09:36] of the first super computers.
[09:37] It is crazy to think we have
[09:39] come this far with  where you
[09:41] can have a data center on your
[09:44] desktop.
[09:44] We are extending the developer
[09:47] end points to the cloud.
[09:48] Windows 365 has the developer
[09:51] distribution that is optimized
[09:53] for developer productivity in
[09:55] the cloud that's windows 365
[09:59] that I use every day.
[10:01] It is about making windows
[10:02] whether it is on the laptop,
[10:04] desktop, the cloud wherever the
[10:06] best place to build.
[10:07] And so to that end we have tons
[10:10] and tons of updates.
[10:12] We have starting  -- we are
[10:15] starting with the favorite
[10:16] things for all of us, which is
[10:19] distraction 3 dev environment.
[10:21] We are introducing an
[10:23] intelligent terminal that has
[10:25] built in GitHub Copilot.
[10:29] Copilot intelligence.
[10:30] Of course lots and lots of Linux
[10:34] love for windows now.
[10:38] You have 70 plus utilities.
[10:38] Grab in full glory is now
[10:41] available for regular windows
[10:43] access.
[10:45] 70 plus utilities as I said.
[10:46] All of that is coming to
[10:48] windows.
[10:48] We are also bringing that you
[10:58] love.
[10:58] Home brew will be on native as
[11:16] well.
[11:17] Switching all of this.
[11:18] Having first class support for
[11:20] containers while  will hp  --
[11:26] will really help us when
[11:28] developing locally.
[11:29] Showing you all of this on new
[11:35] surface on RTX.
[11:35] I wanted to invite up on stage
[11:38] Kayla to take a spin through all
[11:43] of the Dev tools.
[11:45] Kayla, go ahead.
[11:46] [Applause]
[11:47] >> Our team has been working
[11:48] hard to make Windows a great
[11:51] place for development.
[11:52] Today I am going to show you
[11:54] major improvements and
[11:55] delighters you can try out
[11:57] today.
[11:57] What we have here is the default
[11:58] experience on the Surface RTX
[12:02] Spark DevBox.
[12:04] It is calm.
[12:05] No news feed no widgets popping
[12:08] up no notifications.
[12:09] We are in dark mode of course.
[12:11] I am ready to start development.
[12:14] There's one thing I would like
[12:14] to change.
[12:15] I like to put my task bar on the
[12:18] left.
[12:18] Let me jump into my task bar
[12:22] settings using run which is
[12:23] built with power tools xand
[12:27] pallet.
[12:28] No I want it all of the way on
[12:29] the left.
[12:29] There we go.
[12:30] After popular demand we are
[12:32] excited to announce vertical
[12:35] task bar is available in Windows
[12:37] insider build.
[12:39] The new surfaSurface RTX Spark
[12:43] bunch of tools already installed
[12:46] like python and node.
[12:48] All developer goodness all in
[12:51] one file.
[12:51] If you want the same experience
[12:53] on your device we are making
[12:55] this file available to everyone
[12:57] right now.
[12:57] We have a public repo set up
[12:59] with configuration file and how
[13:01] to apply it.
[13:03] It will make adjustments to
[13:05] windows and install all of the
[13:07] tools.
[13:08] One cool thing I have running
[13:08] here is power toys new utility
[13:12] called grab and move.
[13:14] Hold ality and move the window
[13:16] around from anywhere.
[13:16] You can enable end task which
[13:19] let's you end the process
[13:21] without having to open task
[13:23] manager.
[13:23] Let me jump into my Dev drive.
[13:26] It runs with defender running in
[13:31] async and authorize performance
[13:33] when comes to developer
[13:35] scenarios.
[13:35] Get aware.
[13:37] We have stuff like last name
[13:40] change message status of each
[13:42] file and the branch name is on
[13:43] the bottom left.
[13:45] So now let's get started
[13:47] building and open our terminal.
[13:54] It helps working with agents
[13:56] even more seamless.
[13:57] When you install intelligent
[14:01] terminal you can pick your
[14:02] favorite agent.
[14:02] I am using GitHub Copilot today.
[14:05] You can use which ever agent
[14:06] speaks to you.
[14:07] In intelligent terminal I have
[14:10] terminal pane at the top and
[14:12] agent working on the bottom.
[14:13] I, work between them while the
[14:14] agent helps along the way.
[14:16] Here is an air being generated.
[14:18] Agent pain is able to detect it
[14:20] and provide a fix which is great
[14:26] when I don't understand the sin
[14:27] tax.
[14:28] I am working on open claw I have
[14:30] worked it using WSL container.
[14:32] It is a native container
[14:34] experience on Windows plus it
[14:37] can leverage the GPU.
[14:40] It can reference your existing
[14:42] container files just like the
[14:45] one in open claw project.
[14:47] This is open in Microsoft edit
[14:49] got sin tax highlighting and
[14:52] latest version.
[14:53] You can see the container
[14:56] running with container command.
[14:59] Since we are on the topic of WSL
[15:03] there is a profile that is
[15:04] comfortable for those using
[15:07] tools like star, CSH and home
[15:11] brew.
[15:11] It has all of the favorite
[15:13] utilities and available in the
[15:14] repo I showed earlier and
[15:16] includes B top one of my
[15:19] favorites.
[15:19] The Surface RTX Spark serves
[15:24] large local models for coding.
[15:27] I have done development with 120
[15:31] billion parameter model which
[15:32] most machines can't load.
[15:35] We can see how many tokens I
[15:37] have used locally.
[15:41] 3.4 million tokens leveraged on
[15:43] the device itself.
[15:44] We can kickoff agents using
[15:46] fleet.
[15:46] Just so you don't have to watch
[15:48] me type I am going to use
[15:49] Copilot voice feature which is
[15:51] leveraging its own local model.
[15:52] I will hold face for it and tell
[15:55] it what I want it to do.
[15:57] Find any console dot right line
[16:00] calls in the tray and node
[16:03] projects and convert them to the
[16:04] standard logger used elsewhere
[16:07] in the code base.
[16:08] There we go.
[16:10] The main agent will delegate sub
[16:12] agent tasks of appropriate
[16:14] complexity to the local model
[16:15] use liesing GPU and making it
[16:19] cost efficient.
[16:19] As developers while debugging we
[16:22] are looking through log files to
[16:24] diagnose any issues.
[16:25] Finding the location of the log
[16:27] files is a challenge.
[16:28] I would love to be able to type
[16:30] something like grep log and find
[16:32] them all.
[16:33] Sweet.
[16:34] On top of already adding curl,
[16:37] car and pseudo to windows now we
[16:39] are adding over 75 command line
[16:42] utilities like end, be head,
[16:43] tail and touch for those of us
[16:45] who love to live in the
[16:48] terminal.
[16:49] So I found all of my log files,
[16:51] but now having to pars them is
[16:53] the second challenge.
[16:54] I have had eye on instruct
[16:57] practically performing analyses
[16:59] on my log files this whole time.
[17:01] Now I can quickly diagnose
[17:03] anything that has gone wrong in
[17:04] my development, plus I don't
[17:07] have to worry about token usage
[17:09] since it is all local.
[17:10] We can take a look at the
[17:12] machinery sources.
[17:13] Models are loaded you can see 90
[17:15] gigs of ram being utilized by
[17:18] the GPU using the full power of
[17:23] RTX Spark.
[17:25] We were able to use them
[17:27] simultaneously unmetered going
[17:28] through the dev flow without a
[17:31] hitch.
[17:31] That's huge.
[17:36] [Applause]
[17:38] So I know you are going to love
[17:39] what the team has been working
[17:41] on.
[17:41] We hope it gives you a glimpse
[17:43] of what's possible on windows
[17:45] today and where we are headed
[17:46] next.
[17:46] Thank you.
[17:47] Back to you Satya.
[17:57] >> That's the beginning of
[18:01] unmetered intelligence having
[18:02] the models and agents using the
[18:04] models work in parallel to what
[18:07] you are doing in the cloud.
[18:08] That's what the platform enables
[18:11] as first class.
[18:12] Let's move to the cloud.
[18:13] The driving equation for us
[18:15] remains the same, which is
[18:16] tokens per dollar per watt.
[18:18] How do we optimize around this?
[18:21] When we think about the systems
[18:22] problem we think about electrons
[18:24] coming on one end and tokens on
[18:26] the other end and how do we
[18:28] think about the systems
[18:30] optimization end-to-end.
[18:31] It starts with the data center
[18:34] design itself.
[18:34] The core compute, storage,
[18:36] network.
[18:37] All of the accelerators that are
[18:39] going to accelerate each of the
[18:41] components.
[18:42] How do you think about the DC to
[18:45] DC connectivity and networking
[18:47] as well as the off load to
[18:49] something like the local
[18:51] compute.
[18:51] That's the sort of systems
[18:54] challenge.
[18:54] But before we even get into all
[18:56] of the systems and the
[18:57] technology and the innovation,
[19:00] perhaps the most important
[19:01] design criteria for us is how do
[19:04] we earn the permission from the
[19:07] communities in which we are
[19:08] building these data centers.
[19:10] That's where these principles
[19:12] ground us and focus us.
[19:14] How do we ensure that the DCs do
[19:17] not increase the electro city
[19:22] prices.
[19:23] Making sure we are using water
[19:27] use.
[19:28] Creating jobs for the local
[19:30] residents.
[19:30] Adding to the tax base.
[19:32] Making sure we are strengthening
[19:33] communities by investing in had
[19:35] local training and the
[19:36] nonprofits in the area.
[19:40] Only when we live up to the
[19:42] principles do the hard work is
[19:46] when we innovate and build.
[19:48] We have been doing a lot of data
[19:49] center build up.
[19:52] Today Azure spans more than 500
[19:57] data centers in the region.
[19:58] We have the most expansive
[20:01] hyperscaler footprint out there.
[20:02] We have added more data center
[20:04] capacity in the last 18 months
[20:08] than the first decade of to put
[20:14] that in perspective.
[20:23] We built out the cloud
[20:25] infrastructure for hetero genius
[20:29] workloads that span the
[20:30] enterprise.
[20:31] When you look at what we are
[20:32] building when you think of all
[20:33] of the gigawatts that are going
[20:35] to come on-line we have three
[20:39] dominant workloads.
[20:39] There is training, inference and
[20:42] the agent run time.
[20:42] These are three dominant
[20:44] workloads.
[20:45] In fact when you look at fair
[20:48] water it was sort of our first
[20:51] AI super factory.
[20:53] It spanned two regions, Georgia
[20:55] and Wisconsin.
[20:57] The entire system was designed
[20:59] from the ground up for AI.
[21:00] We worked in fact very closely
[21:03] with inn NVIDIA on this.
[21:05] It is a two-story architecture
[21:08] that lets us he goes --
[21:13] essentially put the number of
[21:15] GPU densely with network access.
[21:18] Means you really got fantastic
[21:20] higher performance networking,
[21:22] lower latency and more band
[21:25] width across the entire cluster.
[21:27] We are rethinking even the power
[21:29] delivery.
[21:30] Pou do  -- how do we deliver 100
[21:38] kilowatts her row with the
[21:39] conversion loss to the grid to
[21:41] silicon.
[21:42] We took a new approach to it.
[21:46] Also changes with the cooling
[21:48] system and water.
[21:51] The tooling loop is filled and
[21:54] the data center can operate
[21:57] effectively with zero water
[21:59] consumption.
[21:59] The daily water usage over the
[22:02] course of the entire one year is
[22:05] equivalent to what the single
[22:07] restaurant would use.
[22:12] [Applause]
[22:13] When it comes to the systems in
[22:15] the silicon, we have a lot of
[22:18] choice.
[22:18] We have first party silicon, we
[22:20] have partner systems.
[22:24] We have the first cloud to bring
[22:27] up inn  NVIDIA.
[22:32] We work close with AMD.
[22:34] Working with them on the NX
[22:38] generation AMD GPU.
[22:42] Meyer 200 is continuing to scale
[22:43] it is live in Arizona will
[22:48] deploy internationally.
[22:49] It delivers 30% per tokens per
[22:53] dollar verses what's is the
[22:55] leading GPU today.
[22:56] We have validated it with GPD55
[22:59] and we are going to use that to
[23:02] power Microsoft 365 Copilot.
[23:03] When it comes to running these
[23:09] agents, interesting thing now it
[23:11] is no longer about having AI
[23:15] accelerator or GPU.
[23:16] CPU is critical.
[23:18] Ratios may be coming to one is
[23:21] to one.
[23:22] That's why we are innovating
[23:25] with cobalt.
[23:25] We are announcing preview Of
[23:29] Co
[23:30] Cobalt00VMs.
[23:31] Next generation on generation
[23:32] CPU designed for cloud native
[23:34] and agent workloads today.
[23:36] It is exciting to see Cobalt
[23:39] make progress as well.
[23:45] [Applause]
[23:46] One thing we are trying to make
[23:48] sure being optimized for the new
[23:50] agent workloads.
[23:53] Cobalt delivers 30% better than
[23:57] Cobalt on cloud native.
[24:00] Using GitHub Copilot traces
[24:02] these agentic traces to see.
[24:03] The call patterns are so
[24:06] different.
[24:06] We are seeing 33% lower latency
[24:09] for the agent calls, 14% faster
[24:13] speed, 23% higher through put.
[24:14] This is about the co design of
[24:15] both the AI accelerator and the
[24:18] CPU for the agent.
[24:20] And of course, when you talk
[24:23] about AI workloads you need
[24:24] scale, you need reliability and
[24:27] that's why the network becomes
[24:29] super critical.
[24:32] We have innovated with the
[24:35] architecture and rebuilt how
[24:38] traffic across Azure moves to
[24:40] support workloads which is
[24:43] synchronous data workloads.
[24:46] They span 10s of thousands of
[24:48] GPUs.
[24:49] You need to have them coherent.
[24:52] That's what the next frontier is
[24:53] to make sure we are able to keep
[24:55] scaling the network
[25:04] architarchi
[25:05] architecture.
[25:06] It is not just about outside it
[25:06] is inside the data centers.
[25:08] It is connected through our
[25:12] continent spanning AI with the
[25:17] truly fungible compute fabric.
[25:19] All of this innovation is
[25:22] exciting, but when you think
[25:26] about innovation designing for
[25:27] AI and I would say deep
[25:29] understanding of systems,
[25:32] there's no better company than
[25:37] NVIDIA and there's no other
[25:38] person than Jensen to talk about
[25:41] it.
[25:42] I want to invite Jensen Wang
[25:55] from NVIDIA.
[25:57] I know it is late for you in
[25:59] Taipei.
[26:00] I appreciate you staying up.
[26:04] I have been looking at social
[26:15] and people talking about your
[26:17] keynote.
[26:18] The concept of unmetered
[26:20] intelligence right at the edge
[26:21] is so hot again.
[26:23] You have thought about this,
[26:25] talked about this, now of course
[26:26] with RTX Spark really delivered
[26:31] what is a break through system
[26:33] for AI to be much Moreau  --
[26:38] more ubiquitous.
[26:39] Maybe you can share where you
[26:40] see this going?
[26:42] >> It all started about three
[26:44] years ago with a conversation
[26:45] between you and I.
[26:46] We were talking about how we
[26:49] could build a new class of PCs
[26:51] incredible for designers and
[26:53] creators and it would be
[26:55] incredible for artificial
[26:56] intelligence.
[26:57] It would be one of these systems
[26:59] that has the processing
[27:01] capability but also the software
[27:04] stack that is integrated into
[27:05] the world's design packages and
[27:09] creator packages and all of the
[27:10] things we are doing with AI.
[27:12] Here we are three years later we
[27:13] built an incredible new chip,
[27:16] and this system is supported by
[27:21] all of this new software you
[27:26] created for Windows.
[27:29] We have autonomous agent running
[27:30] on the PC.
[27:31] When you step back and think
[27:33] about what does that mean?
[27:35] For 40 years or some 30 years we
[27:37] have been working together we
[27:40] went from inventing direct X
[27:43] together to creating now this
[27:46] incredible computer that has
[27:48] autonomous systems running.
[27:50] The PC evolved from being an
[27:53] incredible tool to now being a
[27:54] tool that is used autonomously
[27:57] by an AI assistant.
[28:04] The idea I could be traveling
[28:06] and on the phone and text my PC
[28:08] and ask my PC to get coding done
[28:11] or an idea I have and it would
[28:13] fire up the tools on the PC and
[28:15] it would make the modifications
[28:16] or the changes or the design
[28:18] that I told it to do, and it
[28:20] would iterate with me while I am
[28:22] away from the PC.
[28:24] My PC became an assistant.
[28:30] While I am sitting there it
[28:31] would my great system as well.
[28:33] The idea that the PC evolved
[28:35] from a personal computer to
[28:37] personal AI is really exciting.
[28:44] To see it come to life and
[28:46] actually doing that, I am super
[28:48] excited about it.
[28:49] Spark you mentioned earlier has
[28:51] all of these capabilities Peta
[28:53] flop of AI performance.
[28:55] Has a Peta flop of MVFP4
[29:02] numerical format our companies
[29:02] worked on together that allows
[29:04] it to take advantage of the 128
[29:07] giga bites of memory and fit
[29:08] maybe a couple hundred billion
[29:11] parameter model.
[29:12] A couple hundred billion
[29:14] parameter model is state of the
[29:16] art.
[29:16] The days of having a really
[29:17] smart assistant running on the
[29:20] PC is here.
[29:23] >> It is awesome.
[29:24] I am excited about Windows
[29:27] coming to the GP300.
[29:31] It is a data center on your
[29:35] laptop.
[29:35] Talking about the data center
[29:37] side.
[29:37] This entire thing got started
[29:40] when we built the first super
[29:43] computer to train the GPU
[29:47] models.
[29:47] I was talking about the fair
[29:49] water design, this custom build
[29:52] for the grace black water era to
[29:55] Max the data center design with
[29:56] the system design you had.
[29:58] Now of course we are validating
[30:02] be
[30:04] be
[30:05] belly --.
[30:06] Maybe you can share on the clout
[30:08] side how you are sharing the
[30:12] system.
[30:12] >> Our journey is incredible.
[30:13] We built the first AI super
[30:16] computer together.
[30:17] Hopper was an incredible
[30:20] success.
[30:20] The first two generations were
[30:23] focused on free training.
[30:25] Grace Blackwell came along and
[30:27] all of the focus moved to post
[30:29] training, reinforcement learning
[30:31] which allowed us to have
[30:33] reasoning models.
[30:34] These reasoning models based on
[30:35] mixture of experts were
[30:38] incredibly intelligent, energy
[30:40] efficient.
[30:41] It requires giant systems.
[30:43] We required MV length 72.
[30:46] The entire rack became one
[30:48] computer.
[30:49] We had evolved from one node to
[30:51] now one rack.
[30:55] Well, Microsoft deployed the
[30:56] largest number of grace
[30:58] Blackwells in the world today.
[31:02] Largest number of grace
[31:03] Blackwells in the world.
[31:07] Fair water is a magnificent
[31:09] system to look at.
[31:13] It is an incredible feet.
[31:15] It is liquid cooled.
[31:17] Something you mentioned it is
[31:18] close looped, basically uses
[31:20] almost no water.
[31:21] It is incredibly environmentally
[31:25] friendly energy efficient.
[31:27] We are able to increase the
[31:29] token generation rate and reduce
[31:31] the cost of token generation by
[31:33] an order of magnitude, some 30
[31:36] times over hopper.
[31:36] That was a huge achievement.
[31:39] Vera Ruben was created for a
[31:42] world where these AIs are now
[31:45] agentic.
[31:45] As hopper was created for
[31:48] pretraining grace Blackwell for
[31:49] training, post training and also
[31:55] inference.
[31:57] Vera Rubin is designed to run
[31:59] for agents.
[32:00] It is the same computing pattern
[32:03] when run on the RTX Spark.
[32:06] It is the same agentic system
[32:08] but of course larger win process
[32:13] enormous number of them.
[32:14] Many will be from different
[32:15] customers and different
[32:19] partners.
[32:19] The entire path, coding path
[32:22] from storage which is the
[32:23] long-term memory, working memory
[32:26] is encrypted.
[32:27] The data is encrypted in transit
[32:31] and data also incen crepted in
[32:38] encrypted in use.
[32:38] This entire disaggregated
[32:41] distributed computing system you
[32:43] mentioned CPU Vera is a CPU
[32:46] designed for agents.
[32:48] In the past CPUs were designed
[32:51] for humans.
[32:52] We are more parent than agents
[32:55] are.
[32:55] Agents want low latency.
[32:57] Vera was designed for extremely
[33:00] low latency.
[33:03] Vera Rubin I can't wait to show
[33:07] everybody.
[33:07] You stood it up.
[33:08] Our two teams have been working
[33:13] closely.
[33:13] Long before the chips taped out
[33:16] long before the systems brought
[33:18] up the two teams were almost
[33:21] completely aligned.
[33:23] Data centers created for Vera
[33:27] Rubin.
[33:28] It is into the networking and
[33:29] stack and community.
[33:30] The moment systems were rolling
[33:32] off the lines they were building
[33:34] stood up at Microsoft.
[33:37] So incredibly excited about the
[33:38] collaboration.
[33:39] >> This speed of light execution
[33:40] for the teams are fantastic to
[33:42] see.
[33:42] All of this is to power the
[33:45] ecosystem around us.
[33:46] You and I having grown-up with a
[33:48] PC the server and now with AI,
[33:50] have always thought about
[33:52] ultimately it is about creating
[33:54] the opportunity for every
[33:55] developer, every organization to
[33:57] build on the work that we do and
[33:58] the platforms we create.
[34:00] Speaking of that there's a lot
[34:02] of software that vid NVIDIA
[34:08] builds we will have the models
[34:10] in foundry, tooling in foundry.
[34:13] Software will help us with
[34:15] accelerating our workloads when
[34:16] it comes to the data warehouse.
[34:20] We will have the stuff in
[34:22] windows.
[34:23] Talk about the broader vision of
[34:24] what does it mean as an
[34:28] opportunity.
[34:28] Everybody talks about the one
[34:31] model and one piece of tech.
[34:32] It is about the broadest biggest
[34:36] opportunity maybe you want to
[34:38] share a little bit about that.
[34:41] >> We have been preparing for
[34:42] this moment.
[34:43] What happened the last several
[34:45] months we have been working a
[34:47] decade and a half together
[34:47] getting ready for really what
[34:49] happened in the last several
[34:50] months.
[34:50] All of a sudden because of
[34:52] agentic systems the convergence
[34:54] of these great models AI is now
[34:57] useful.
[34:57] If you look at GitHub, the
[35:03] commits into GitHub has gone
[35:05] completely parabolic.
[35:07] In the last several months the
[35:09] number of commits increase by a
[35:11] factor of three.
[35:13] It is clear agentic systems are
[35:15] useful, it is doing productive
[35:17] work and also it is profitable
[35:23] as a result.
[35:23] The amount of command for
[35:25] compute between the usage of AI
[35:29] the contribution necessary for
[35:32] agents it has gone through the
[35:35] roof.
[35:35] One thing we have been doing
[35:36] together is making sure all of
[35:38] the tools agents are using are
[35:42] fully accelerated.
[35:43] Fabric is fully accelerated.
[35:46] We are accelerating data
[35:49] processing, SQL, Spark, semantic
[35:54] based, graph based we are going
[35:55] to make sure all of the tools
[35:57] available on Azure are going to
[35:59] be fully GPU accelerated because
[36:01] the agents are going to be
[36:05] impa
[36:05] impatient.
[36:05] The faster it can iterate and
[36:08] generate tokens which are
[36:09] ultimately what the developers
[36:12] both customers would like to do
[36:13] is generate a lot of tokens that
[36:16] are profitable and highly
[36:18] intelligent.
[36:19] >> Thank you so much for the
[36:20] partnership and leadership and
[36:22] innovation that you bring to
[36:23] this entire ecosystem and really
[36:25] thrilled to be working closely
[36:27] with you and the team and bring
[36:29] all of this to the developers
[36:30] here and beyond and look forward
[36:32] to seeing what the next sort of
[36:34] few months and the next year
[36:36] will bring in terns of the
[36:38] innovation that gets built on
[36:40] top of the platform.
[36:41] Thank you again for joining this
[36:42] late in the night from Taipei.
[36:45] >> Thank you so much Satya for
[36:47] your partnership and friendship.
[36:49] [Applause]
[36:53] >> So so far we have talked
[36:57] about the edge and the cloud.
[37:00] The form factors, when I saw
[37:02] that Jensen picture from the
[37:04] weekend where he had all of the
[37:06] desktops.
[37:07] I felt man I am back in the 90s.
[37:09] It is so tool to see the lineup
[37:11] of all of the machines that I
[37:13] loved and grew up with back yet
[37:15] again with new functionality.
[37:17] The same form factor, but
[37:20] unbelievable new functionality,
[37:22] because of the on board AI
[37:24] capability.
[37:25] That is sort of what we have
[37:26] seen with the laptop, the
[37:28] desktop and of course with the
[37:30] cloud.
[37:30] But it also, you know, sets up
[37:33] that next question.
[37:35] If you have that capability,
[37:38] which is new function and you
[37:39] can put it into existing form
[37:42] fac
[37:43] factors, can you even purpose
[37:45] build new form factors for the
[37:49] new function?
[37:49] Can you build a new platform
[37:52] even for the agent era?
[37:54] And that is the motivation
[37:57] behind project Solara.
[38:02] I wanted to invite Stevie on
[38:05] stage but first let's roll the
[38:07] video.
[38:08] >> First there's you.
[38:14] Then everything in front of you.
[38:15] We get it.
[38:16] The noise, the weight of it all.
[38:22] We have been building something.
[38:30] For an agent first world.
[38:31] New agent first devices.
[38:33] You come close, it is ready when
[38:38] you are.
[38:38] You speak, it understands you.
[38:40] What matters comes forward.
[38:44] Everything else falls away.
[38:46] When you want it to keep going
[38:49] it keeps going.
[38:57] Sometimes it sits with you.
[38:59] Sometimes it goes with you.
[39:00] Sometimes it sees what you want
[39:03] it to see.
[39:04] It doesn't decide for you.
[39:06] It lights the path.
[39:08] Tries to clear the way when you
[39:10] need it to.
[39:11] That's the idea.
[39:15] A whole ecosystem built to clear
[39:19] the way.
[39:25] Wherever you are, whenever it
[39:29] matters.
[39:30] >> I am so excited to be here.
[39:33] It really is great to be back on
[39:35] this stage.
[39:36] Before I talk about those
[39:37] awesome new devices you just
[39:39] saw, let me start with the why.
[39:43] Back at build 2023 I talked
[39:48] about the outside AI
[39:50] infrastructure.
[39:51] It moves within the application
[39:55] frame to operating globally to
[40:00] connect maintain context across
[40:02] entire workflows, devices and
[40:04] time scales.
[40:09] What if there were an ecosystem
[40:11] of devices specifically designed
[40:13] for the new type of application
[40:16] structure for those types of
[40:18] agents for that transaction.
[40:22] That's the impetus behind
[40:24] project Solara.
[40:27] With many possible forms which
[40:29] one do you pick?
[40:30] What is the next device.
[40:32] The big uh-huh for us is that it
[40:34] is not choosing one form factor
[40:37] it is about creating a system
[40:39] that extends your agent across a
[40:43] cons  constellation of devices.
[40:47] The next computer is all of the
[40:49] devices working together as one
[40:51] system with agents cloeing up
[40:52] closer to where and when you
[40:55] need them.
[40:56] To realize this vision two
[40:59] challenges immediately show up.
[41:03] First many specialized form
[41:05] factors already exist but often
[41:07] rely on custom one off apps and
[41:10] fragmented stacks that are
[41:11] difficult and expensive to
[41:13] build, deploy and maintain.
[41:16] Second across every industry
[41:16] people and organizations are
[41:18] already building their own
[41:20] agents deeply specialized and
[41:23] instrumented for their work.
[41:24] The impact of those agents is
[41:27] constrained by how and where
[41:28] they can exist.
[41:31] Project Solara does both by
[41:35] extending agents on to new
[41:37] purpose built easy to manage
[41:39] form factors designed to reach
[41:41] the nooks and crannies where
[41:44] project creators do not exist or
[41:46] are not optimal.
[41:48] It is a turnkey solution for
[41:50] building unique agent first
[41:53] devices enabled by three
[41:55] pillars.
[41:56] First it is enterprise ready.
[41:58] Enabled by the AOSP based
[42:01] Microsoft device ecosystem
[42:05] platform.
[42:06] Second it has an agent driven
[42:07] interaction model with just in
[42:09] time UI that adapts to the form
[42:11] factor.
[42:13] Third it has extensibility so
[42:14] you can bring your own agents.
[42:17] Tying it all together is Azure
[42:19] unifying the system across cloud
[42:20] and device.
[42:22] That's enough of that.
[42:23] Now let's talk about the
[42:26] devices.
[42:26] Today we are previewing two very
[42:29] broad categories.
[42:30] The first is stationary and the
[42:32] second is port able.
[42:33] The first device is designed for
[42:35] your desk.
[42:36] It is built on media tech
[42:39] silicon.
[42:39] With hello for business walking
[42:40] up to the device securely signs
[42:43] you in giving you direct access
[42:45] to your agent.
[42:46] Just like Nathan is about to
[42:48] show you here.
[42:49] For the information work this
[42:53] means frictionless yet protected
[42:55] access to Microsoft 365 Copilot
[42:58] grounded in work IQ.
[43:00] What matters next in your
[43:02] workday helping you think, plan
[43:04] and even act by delegating task
[43:06] to your agent with a simple tap
[43:09] or just using your voice.
[43:16] Dedicated Ambian device for
[43:19] work.
[43:19] It acts as campaign onto your
[43:21] existing windows Copilot Plus
[43:25] -- PC.
[43:26] Or access windows 365 in a
[43:31] connected monitor.
[43:34] How cool is that?
[43:38] [Applause]
[43:39] The second is port able.
[43:41] Re-imagining a wear able
[43:42] millions of people use every
[43:43] day.
[43:44] The access badge.
[43:47] Built using Qualcomm silicon
[43:50] wear able this badge is light
[43:51] weight form factor designed for
[43:54] agents on the go.
[43:56] Adapt able across variety of
[43:59] vert
[44:01] verticals  and workflows.
[44:02] I have an early prototype of the
[44:08] badge.
[44:08] Using my finger print I tapped
[44:10] to unlock the device now I have
[44:12] all of my agents in a secure
[44:16] manner.
[44:17] Gather content for social media
[44:19] post for today.
[44:22] Why not do it right now.
[44:23] I am going to hit record and the
[44:26] device's camera is recording.
[44:31] Hope you don't mind I am going
[44:32] to take shots.
[44:34] Find good shots from this clean
[44:36] them up and send them for me and
[44:38] my team to review.
[44:41] There you have it.
[44:42] Now my agent is off running
[44:44] through multiple tasks I clean
[44:47] them up and send them to me and
[44:50] the team.
[44:53] Pretty cool.
[44:54] I know it is a simple demo but
[44:56] it is all agent driven and there
[44:58] are so many verticals and
[45:01] opportunity.
[45:01] Imagine in healthcare from the
[45:03] moment you pick up the device
[45:05] the right agent shapes the
[45:07] experience around the roll and
[45:08] the workflow helping with check
[45:10] ins, patient records and
[45:12] critical insights.
[45:13] All through enterprise grade
[45:16] secure access.
[45:17] And with the built in
[45:18] microphones the nurse can start
[45:21] a hands free voice based
[45:23] documentation including
[45:25] dirizezation and and notation.
[45:27] The side phasing camera can help
[45:29] with asht vitals or scan
[45:32] medications or help verify
[45:34] workflows.
[45:34] This is a few examples of how
[45:36] the small purpose built wear
[45:38] able can bring intelligence
[45:40] directly into the flow of
[45:41] patient care helping nurses
[45:43] access and gather and even act
[45:46] on information while staying
[45:47] print with the patient.
[45:55] Both devices are an expression
[45:58] of devices the core hardware and
[46:00] software are designed to be
[46:02] highly flexible.
[46:02] With a few changes loading a
[46:04] different agent adjusting the
[46:07] shape, screen size, sensors or
[46:09] input methods the same
[46:10] foundation, the same software
[46:12] can be adapted for many
[46:14] verticals and workflows such as
[46:16] retail, industrial, hospitality,
[46:19] financial service, legal and so
[46:20] forth.
[46:21] That is the power of the
[46:23] platform, that flexibility.
[46:28] Whatever scenario there are
[46:30] places where computing has not
[46:33] naturally fit before.
[46:33] While this is an early look, we
[46:36] are really excited that ak ue
[46:42] whether, best buy, target and
[46:44] others are working towards
[46:46] exploring how devices can
[46:48] improve their workflows.
[46:49] This is the broader opportunity
[46:51] for the ecosystem.
[46:54] Agents moving outside of the app
[46:55] and taking shape in devices
[46:58] designed for specific scenario,
[47:00] a specific customer and a
[47:02] specific place.
[47:05] For all of you this is the
[47:07] moment to imagine where your
[47:10] agent should live, what form
[47:12] they should take and what new
[47:14] work they can unlock.
[47:19] Last week Satya sat down with
[47:24] Qualcomm, Chrissian yaw to talk
[47:28] about the future.
[47:30] >> Incredible to have you at
[47:32] device lab and talk about the
[47:33] reference designs.
[47:34] >> Happy to be here.
[47:35] I have been to the device lab a
[47:38] lot recently.
[47:38] >> One of the things that you
[47:39] and I have chatted for a while
[47:41] is how there's a real platform
[47:43] shift.
[47:44] We are moving from building
[47:45] operating systems, devices for
[47:50] apps for agents.
[47:50] You want to talk about how you
[47:53] see this?
[47:57] >> This is technology this one
[47:59] seems to be a big one.
[48:01] Agents change the whole nature
[48:03] of the device in itself.
[48:07] Starting with the fact that if
[48:09] AI understands the world as we
[48:11] understand it it will be closer
[48:12] to our senses, closer to our
[48:14] eyes and mouth and ears.
[48:18] It will be things we wear.
[48:21] You need computing which is
[48:24] geared towards real time
[48:27] context.
[48:27] And from silicon to cloud.
[48:30] >> This is one of the references
[48:31] we have that I love.
[48:34] There is stuff happening 07b9
[48:35] edge here.
[48:37] Things on the cloud.
[48:37] You want to talk about the core
[48:39] systems and silicon implications
[48:41] for such a new ecosystem.
[48:44] >> You need a power efficient
[48:47] CPU.
[48:47] The silicon is designed for you
[48:49] to have a cloud native
[48:51] experience.
[48:51] You have a lot of sensors for
[48:56] context.
[48:56] It is personaized.
[49:01] It is a thank  change in the
[49:04] nature of devices.
[49:06] Wear able platform is changing
[49:08] itself.
[49:09] You see incredible factors.
[49:12] >> The computer platform, how do
[49:15] you build it such that there is
[49:18] an open ecosystem.
[49:22] It is not about one agent it is
[49:25] about any agent.
[49:28] >> Because the smartphone today
[49:30] is at the center of your digital
[49:32] life, the job of those devices
[49:35] today is to be extending the
[49:40] functionality of the smartphone.
[49:42] You saw many platforms became
[49:45] vertical.
[49:45] It is a natural thing to have a
[49:47] vertical platform from the same
[49:49] company.
[49:50] The phone was at the center.
[49:51] That changes dramatically when
[49:54] you think about agents.
[49:57] Agents becomes the center of
[49:59] your digital experience.
[50:02] They will be looking for an open
[50:05] horizontal platform that enable
[50:07] the agents to be interacting
[50:09] with the best possible device
[50:12] for different applications.
[50:16] >> We want to make it possible
[50:18] for anybody working with agentic
[50:20] system not just bound to devices
[50:23] but imagine they can be many
[50:25] devices that carry that
[50:27] intelligence in different
[50:28] context.
[50:28] That's the open ecosystem we
[50:32] want to build together.
[50:34] It is great to partner with you
[50:36] to get this started.
[50:38] >> We are proud of this
[50:41] partnership and that's just the
[50:46] beginning.
[50:46] >> Thank you very much Steven
[50:49] and Christiano.
[50:51] >> It is a new platform but it
[50:53] is a set of new platform rules
[50:57] that don't in some sense hem in
[51:01] what you can imagine the type of
[51:05] form factors where your agents
[51:08] live.
[51:08] Where the new platforms come you
[51:10] get to rewrite even the rules of
[51:13] how new platforms operate.
[51:15] That's what we are trying to get
[51:17] done with project Solara so you
[51:19] as developers and enterprises
[51:22] have the flexibility to imagine
[51:24] the form factors that you want
[51:27] and have agents be ubiquitous.
[51:32] Let's go up the stack to the
[51:33] next layer.
[51:34] We are building a new
[51:36] intelligence layer bringing
[51:38] together the models, context as
[51:40] well as the tools.
[51:45] It starts with model choice.
[51:47] Every customer every developer
[51:48] is going to choose the right
[51:51] model for the right task and
[51:55] eval mix.
[51:57] Agency budget and foundry today
[51:59] has over 121 sthou models.
[52:02] The largest model catalog out
[52:06] there.
[52:07] From OpenAI to an thropic.
[52:09] Even MAI models.
[52:12] Last week we brought the OpenAI
[52:14] real time voice models along
[52:16] with Claude Opus 4.8 to foundry.
[52:19] We are continuing to bring these
[52:22] frontier models.
[52:24] The consideration about the
[52:26] models is becoming increasingly
[52:29] key.
[52:29] The last developer counsel to
[52:33] now when you are building the
[52:35] agentic system having this
[52:36] context really shaped right is
[52:38] becoming super important.
[52:40] In fact it starts right at the
[52:42] data tier.
[52:43] The data to date has been built
[52:47] for application that is
[52:49] supported user facing
[52:51] applications.
[52:51] Now you have to change build
[52:53] them for agents again different
[52:56] call patterns even to the data
[53:00] tier.
[53:00] Agents are restoring,
[53:04] retrieving, reasoning, acting
[53:05] and learning.
[53:07] That is what is happening in a
[53:11] continuous loop.
[53:12] They are using Cosmos DB,
[53:17] ChatGPT does, Azure search
[53:21] retrieval of ind -- indices.
[53:26] They started the business logic
[53:29] tier effectively for agents.
[53:31] You have fabric real time
[53:35] intelligence which now brings me
[53:38] to an exciting new service
[53:41] Verizon DB which is fully
[53:45] managed SQL server on Azure.
[53:51] One of the things we wanted to
[53:53] make sure we built ground up
[53:56] Proest gres managed service for
[53:59] higher availability scaleout.
[54:03] It is automated fill over 128
[54:08] terra bites per cluster.
[54:10] The read heavy workloads.
[54:12] You can scale with this managed
[54:15] service.
[54:16] In our internal testing we are
[54:20] seeing Verizon is having 3X
[54:24] through put compared to
[54:29] PostgreSQL.
[54:29] It is critical thinking about
[54:30] the scale you need.
[54:31] The other data workload we are
[54:34] changing dramatically is the
[54:35] data warehouse in a world where
[54:39] agents are constantly querying
[54:44] data, data warehouse is
[54:47] critical.
[54:47] One thing to have it mission
[54:49] critical for users but when
[54:51] agents need analysis done on the
[54:54] fly bringing GPU acceleration to
[54:56] fabric is super key.
[54:58] We are seeing 7X performance
[55:03] gain.
[55:03] It is thrilling to see that AI
[55:07] acceleration.
[55:08] [Applause]
[55:08] So if you sort of have the data
[55:11] tier, the layer about this is
[55:14] the IQ layer that we are
[55:17] building that brings together
[55:19] essentially the model
[55:22] capabilities to -- along with
[55:23] the data.
[55:24] You kind of mix the data and
[55:26] model capability so you can
[55:29] deliver that right context to
[55:31] unlock intelligence.
[55:33] In fact when people talk about
[55:37] token efficiency this is perhaps
[55:38] the most important
[55:42] consideration.
[55:43] If you structure the context
[55:45] right and feed the models you
[55:47] are by definition going to be so
[55:50] much more token efficient.
[55:51] The first domain is the web.
[55:53] Web grounding is so important.
[55:55] You need that fresh, high
[55:58] quality and fast web data.
[56:03] And that's why we are really,
[56:05] really excited to announce today
[56:07] web IQ.
[56:08] [Applause]
[56:12] And web IQ is built on our
[56:16] global infrastructure that is
[56:17] already serving over a billion
[56:19] users.
[56:20] But fundamentally rearchitected
[56:23] for the LLM and agentic
[56:26] workflow.
[56:26] It is model agnostic, MCP
[56:30] native.
[56:31] Plugs into any agent run time.
[56:32] It has web and news, images,
[56:36] video, so agents can ground
[56:38] responses in flesh, verify able
[56:42] content, and web IQ leads across
[56:45] all of the three key criteria.
[56:47] It is best in class in quality,
[56:49] best in class in speed as well
[56:51] as in cost.
[56:52] So we are very, very thrilled
[56:55] about web IQ being in the
[56:57] developer's hands as you build
[56:59] out agentic systems.
[57:02] We are not stopping beyond the
[57:03] web.
[57:04] Every developer wants to ground
[57:06] their agents on what is the most
[57:08] valuable data across the
[57:10] enterprise.
[57:10] We are bringing together
[57:14] foundry, fabric and Microsoft
[57:16] 365 as this unified IQ layer.
[57:21] Continuously updated
[57:24] understanding of your
[57:25] organization to show you all of
[57:26] this rich IQ in action building
[57:30] real life agents here is Elijah.
[57:33] Elijah take it away.
[57:36] [Applause]
[57:36] >> Agents are only as good as
[57:39] the context we give them.
[57:41] Microsoft IQ unifies enterprise
[57:44] intelligence for every
[57:48] organization.
[57:48] I am here at a power utilities
[57:51] control center and will start by
[57:53] running a long running agent.
[57:55] This agent is going to help us
[57:57] assess the current grid
[57:59] operations incident and produce
[58:01] a brief for us so we can respond
[58:04] accordingly.
[58:04] Now I am going to go ahead and
[58:05] kick that off.
[58:06] While that runs let me show you
[58:09] how we got here.
[58:12] We built our agent
[58:16] And its also wired to a Foundry
[58:19] IQ knowledge base.
[58:21] A single grounded source that
[58:23] packages our documents,
[58:25] operational data, and people
[58:26] into context the agent can
[58:28] reason over.
[58:29] After building the agent in
[58:31] Foundry, we published it to
[58:32] Microsoft 365 for the whole
[58:36] team.
[58:36] Let's go see it in action.
[58:38] Now, here in M365, I will start
[58:40] with a question about current
[58:41] events that an ungrounded LLM
[58:43] wouldn't be able to answer.
[58:45] I'm going to ask about current
[58:49] electricity prices in SF.
[58:51] Our agents pulls in the first IQ
[58:55] tool, Web IQ, search built for
[58:57] the AI era.
[58:59] It delivers industry-leading
[59:01] quality, velocity, and
[59:03] efficiency.
[59:03] Web IQ constantly indexes fresh
[59:10] official sources and does a
[59:13] great job with semantic
[59:14] documents.
[59:15] And it give us our answer.
[59:18] Grounded in reality.
[59:20] Not only can we deploy our agent
[59:22] here, but we can embed it.
[59:24] Let's head back to the control
[59:27] center.
[59:27] Let's how we handleeds a
[59:31] previous incident.
[59:33] Can he asked for --
[59:36] Deasked for details at our at
[59:38] risk substations.
[59:39] The power of Microsoft IQ comes
[59:41] when we combine that external
[59:42] knowledge with our own internal
[59:45] enterprise context.
[59:46] For this information our agent
[59:48] pulls in the next layer, Fabric
[59:50] IQ.
[59:52] Here is bright line's grid
[59:55] represented as a tab Rick on
[59:58] theology, an operational of the
[60:00] live grid, and we didn't build
[60:04] this from scratch.
[60:07] It takes models used by millions
[60:08] of customers and lets teams
[60:10] extend them into rich ontologies
[60:12] that helps run the business.
[60:14] This model -- yes!
[60:15] This model is coupled with live
[60:17] telemetry, so it reflects the
[60:20] real operational state of the
[60:21] grid minute by minute.
[60:23] This is what Microsoft IQ means
[60:25] by enterprise intelligence.
[60:27] [Applause]
[60:31] Not data scattered across
[60:33] disparate systems, but a single
[60:35] living model of the business
[60:36] that an agent can reason over.
[60:38] Now, turning back to our agent.
[60:41] We can see that it gave us a
[60:43] table of our most exposed
[60:45] substations.
[60:46] That's the agent query and the
[60:48] ontology you just saw.
[60:49] Now the agent has access to the
[60:51] outside world and our COMBRID.
[60:53] The last -- and our grid.
[60:55] The last piece turns the
[60:56] situation into a response.
[60:58] Our policies and our people.
[61:00] By asking, "What are the steps
[61:02] to respond to a substation
[61:05] trip," we activate the final
[61:08] layer, Work IQ.
[61:09] This is Brightline's response
[61:13] procedure in SharePoint.
[61:15] It's the play book the team
[61:16] reaches for when something goes
[61:17] wrong.
[61:18] And the important thing, the
[61:20] agent isn't working from a stale
[61:23] upload or copied snapshot.
[61:25] It's answering from the same
[61:26] source the team maintains day to
[61:28] day.
[61:28] When the procedure changes, the
[61:29] answer changes with it.
[61:31] No reuploads.
[61:32] No prompt rebuildings.
[61:34] And no stale versions.
[61:35] And critically, this is your
[61:38] knowledge.
[61:38] The assets you create stay with
[61:42] you, no matter what model or
[61:43] agent is reasoning over them.
[61:44] Now, if we return to our agent,
[61:46] we can see the proper way to
[61:48] respond to this issue.
[61:50] Not a generic recommendation,
[61:51] but our play book applied to
[61:55] this incident.
[61:55] Now, we just saw one situation,
[61:57] three questions, and one
[61:59] connected answer.
[61:59] Now let's go check back in on
[62:01] our long-running agent.
[62:03] And ...  oh!
[62:05] Let's check the backup really
[62:06] quick.
[62:08] Moment of truth.
[62:11] And ...
[62:15] Boom!
[62:15] Our task finished!
[62:19] Ha-ha!
[62:19] [Applause]
[62:20] Here we can see every step the
[62:23] agent took.
[62:24] First, beginning with Web IQ,
[62:29] connecting it on the outside
[62:30] world.
[62:30] Second, fabric IQ through
[62:32] foundry, organicing it in the
[62:34] is -- anchoring it in the state
[62:35] of operations.
[62:36] And work IQ, grounding in people
[62:38] and procedures.
[62:39] I triggered this manually, but
[62:40] with Foundry routines, this can
[62:42] run on a schedule, turning a one
[62:44] off response into continuous,
[62:46] proactive execution.
[62:47] And if we look closely, we can
[62:50] see it even use the power of
[62:52] work IQ to alert me directly
[62:54] about the incident.
[62:55] And if we go ahead and check
[62:58] Teams, it sent me a brief
[63:00] notifying me of the situation.
[63:06] [Applause]
[63:07] That's the power of Microsoft
[63:10] IQ.
[63:10] When the crisis its, teams don't
[63:16] chance answer.
[63:16] They get an answer all in one
[63:19] place they can trust.
[63:20] Back to you, Satya.
[63:25] [Applause]
[63:25] >> All right.
[63:27] Thank you, Elijah.
[63:29] And now, you know, let's move up
[63:33] from this context model layer to
[63:36] deploying these agents and
[63:37] thinking about the run time.
[63:39] And, you know, when you're
[63:41] building a first-class agentic
[63:44] system, you need a first-class
[63:46] agent run time and a platform
[63:47] that we are going to ship both
[63:49] with Windows as well as part of
[63:52] Foundry in Azure.
[63:53] We want Windows to be a
[63:55] fantastic place to run and scale
[63:59] agents, and agents effectively
[64:02] are a new execution environment.
[64:03] Right?  It's a new paradigm,
[64:07] even.
[64:07] They reason continuously.
[64:08] They generate and run code
[64:12] dynamically.
[64:13] They take action across files
[64:15] and devices, as well as across
[64:17] the network.
[64:19] Obviously there's a lot of power
[64:20] in it.
[64:21] Right?
[64:22] The fact that it can generate
[64:23] code and act on it, on a
[64:27] long-running agent that's
[64:29] autonomous.
[64:29] But obviously it creates new
[64:30] risk.
[64:31] And that's why today we're
[64:33] introducing Microsoft execution
[64:35] containers, or MXC, which is a
[64:38] new policy layer that lets
[64:41] Windows apply isolation and
[64:44] containment, using AI native --
[64:48] OS native primitives.
[64:49] You need to bake this into the
[64:51] operating system, so that the
[64:54] containment is enforced by
[64:55] policy.
[64:56] You can have process level
[64:58] isolation for lightweight agent
[65:00] actions.
[65:01] You can have session-level
[65:02] isolation for user separation.
[65:05] So Windows and Linux machines,
[65:09] including WSL, for much stronger
[65:13] boundaries.
[65:13] And in fact, if you want full
[65:15] isolation and containment,
[65:16] Windows 365 for agents, for
[65:18] maximum isolation in a separate
[65:20] managed environment,
[65:22] effectively.
[65:23] You can pick the right
[65:24] containment option for the work
[65:28] load, and windows will enforce
[65:30] it.
[65:30] And I think this becomes pretty
[65:31] critical as you think about
[65:33] deploying agents at scale on
[65:35] your Windows desktop.
[65:38] We want to ensure containment is
[65:40] enforced, of course, regardless
[65:41] of who builds the agent.
[65:43] This is why you want to bake it
[65:44] into the operating system.
[65:46] On to that end we're working
[65:48] with many partners to ensure
[65:52] that taint, we're supporting
[65:54] real developer workloads out
[65:56] there and addresses the needs.
[65:58] And NVIDIA is bring Open Shell
[66:03] so Windows, and today, we are
[66:06] really thrilled to announce that
[66:09] Open Claw redundancy on Windows
[66:13] leveraging MXC.
[66:14] [Applause]
[66:16] We are very deeply engaged with
[66:18] the team to make Open Claw run
[66:22] super well on Windows.
[66:24] Let me hand it over to my
[66:26] colleagues scat and Samantha
[66:29] toish -- Scott and smoothened to
[66:30] show you.
[66:32] Scott and Samantha?
[66:33] [Cheers and applause.]
[66:36] >> Hey, friends!
[66:38] Open Claw came out in November
[66:40] of last year, and it took the
[66:41] world by on the stomach.
[66:43] And for the -- by the storm.
[66:45] And I've been using it to stay
[66:46] on top of my health.
[66:48] It can help me manage my blood
[66:51] sugar and gives me
[66:55] notifications.
[66:55] I've got line triaging email,
[67:00] GitHub issues, and buys movie
[67:02] tickets.
[67:03] What are you using your for?
[67:04] >> I turned my Open Claw agent
[67:08] into my triathlon coach.
[67:11] It developed a work back plan
[67:12] for me, using my data to  notify
[67:16] me how I'm progressing and
[67:19] keeping me accountable.
[67:23] >> We're working closely with
[67:24] Open Claw to make them
[67:27] successful and more successful
[67:29] on Windows.
[67:30] We've collaborated on GitHub to
[67:32] bring you an Open Claw app.
[67:34] It will help you set up your own
[67:36] on or connect to existing ones.
[67:40] And the Windows companion, we're
[67:41] going to sandbox the Open Claw
[67:43] tool calls to keep you and your
[67:45] system safe.
[67:46] >> Yeah!
[67:47] You'll see the Open Claw windows
[67:50] app rin running in the
[67:52] background.
[67:53] Right-click on it, Scott.
[67:53] >> All right.
[67:58] >> It looks awesome!
[68:00] It looks like a native windows
[68:05] app because it is.
[68:06] It's got information about I
[68:07] gateway, other machines, my
[68:09] sessions, and my usage.
[68:10] I've also got quick access to
[68:13] things like chat, Canvas, the
[68:15] main dashed, and more.
[68:17] Let's jump into companion
[68:18] settings.
[68:19] Within the app, we've got full
[68:22] chat support with tool calling,
[68:23] and you'll notice in the corner,
[68:26] we've got lots of PERMTHSZ
[68:28] options, along with our sandbox
[68:31] configuration.
[68:31] >> Thepermissions
[68:32] options, along with our sandbox
[68:33] configuration.
[68:33] >> The sandbox is interesting
[68:34] because this is using MXC, and
[68:36] for this, we're going to be
[68:38] using process isolation.
[68:39] Now, newer version of Windows
[68:41] will have more containment
[68:44] options, so keep your eyes out
[68:46] for news.
[68:47] I've got one-click security
[68:49] option settings, but Samantha,
[68:50] talk to me about custom folders.
[68:53] >> Yeah.
[68:54] You've got full support about
[68:55] what files and folders you want
[68:58] Open Claw to have access to, and
[68:59] really granular security
[69:01] features like clipboard access
[69:02] or talking to the internet
[69:05] itself.
[69:05] Now, I've given it read-only
[69:08] desktop -- read-access to your
[69:11] depth folder.
[69:12] Open Claw already has a rich
[69:13] safety layer, augmented more by
[69:20] me or policies by IT.
[69:21] For the purposes of this demo,
[69:24] I'm going to do something scary
[69:26] and ask Open Claw to delete all
[69:28] the fails on your depth -- files
[69:30] on your depth.
[69:31] >> I like a clean desktop.
[69:34] >> You hid all your icons from
[69:35] the audience while we were on
[69:37] stage.
[69:37] I think we need to show the
[69:38] audience who you really are
[69:40] >> That's so disrespectful.
[69:41] I know where everything is.
[69:44] [Laughter]
[69:45] It's just -- don't touch my
[69:47] stuff!
[69:47] I know where they are.
[69:48] Sam, I need to make sure that
[69:49] you're clear that a messy
[69:50] desktop is an organized mind.
[69:52] I'm pretty sure that that's
[69:54] the ...  that's the quote,
[69:55] right?
[69:55] So what we've done, asked Open
[69:57] Claw to delete those files from
[69:58] the Windows node, and the only
[70:00] thing that is going to keep it
[70:01] from happening is MXC because
[70:04] we've turned off all of the
[70:06] layers that Open Claw offers,
[70:07] but our IT, in this case,
[70:09] Samantha, has stet to
[70:14] -- you can see the attempts, and
[70:16] checking the directory, and
[70:19] deleting again.
[70:20] It wants the files gone, and I
[70:22] want them to stay.
[70:25] [Laughter]
[70:25] Ope!
[70:26] No!
[70:26] The read-only sandbox is there.
[70:31] 94 je pegs are still there.
[70:35] They're safe!
[70:36] >> Foiled again!
[70:37] >> Oh, my goodness.
[70:38] So bad.
[70:40] >> We've seen security
[70:43] sandboxing, and brought together
[70:44] all in this alpha release of a
[70:46] Windows companion app.
[70:47] We think this app is a great
[70:48] opportunity to showcase Open
[70:50] Claw on windows, and it's only
[70:52] going to get better in the
[70:53] coming months
[70:53] >> That is right.
[70:54] And, by the way, I want to note
[70:54] that we're doing a all this
[70:57] development work on this calm
[70:59] Windows development machine with
[70:59] all the tools that I love, like
[71:04] WSL, containers, a
[71:05] containment, and I've even got
[71:07] GitHub Copilot with multimodel
[71:09] support ready to go.
[71:10] This is literally how the team
[71:11] and I have been working on the
[71:13] Open Claw app on GitHub.
[71:16] Now, people wonder how this came
[71:18] together.
[71:18] Turns out that over the
[71:20] holidays, I got a DM from this
[71:22] random guy on the intent, and it
[71:25] takes me a couple of days to get
[71:27] back to him.
[71:28] And then when I did, we just had
[71:30] this kind of cool idea.
[71:31] That maybe he should come to --
[71:34] to Microsoft Build.
[71:35] I think there's one more person
[71:36] that we want to thank for
[71:37] bringing the next generation of
[71:38] agents to the world.
[71:40] Everybody put your hands
[71:43] together for the claw father
[71:46] himself, Peter Steinberger!
[71:49] [Cheers and applause.]
[71:50] >> Samantha, it's just showing
[71:52] off my secret DMs!
[71:54] I'm so excited to see Open Claw
[71:58] native on Windows.
[71:59] You know, watching a claw try to
[72:01] delete all your desktop files
[72:04] and just fail makes me really
[72:07] happy.
[72:07] [Laughter]
[72:08] Because six months ago, that
[72:10] totally would've worked.
[72:12] [Laughter]
[72:12] I built Open Claw to have access
[72:14] to everything.
[72:15] My files, my machines, my chats.
[72:18] Always on, and fully open
[72:20] source.
[72:21] That's what makes it so
[72:23] powerful!
[72:23] And that's what also makes
[72:24] companies a bit nervous.
[72:27] You know, what I kept hearing
[72:30] was, "Peter, I love my claw.
[72:32] Can I use this at work?"  And
[72:33] that's what we spent the last
[72:35] few months on.
[72:37] With Microsoft, git Hick, open
[72:41] AI, NVIDIA, just to name a few.
[72:44] We had observability, auto mode
[72:46] for permissions.
[72:47] We changed how access works.
[72:49] It's not all or nothing anymore.
[72:50] You can pick which folder should
[72:53] read only, which ones should be
[72:56] hidden.
[72:56] Here's the nutrition:
[72:57] --
[72:58] Here's the news: .
[72:59] You can run it inside your
[73:01] company now.
[73:01] [Cheering]
[73:04] And we even made the harness
[73:07] itself a plugin.
[73:08] You can bring your own.
[73:11] Copilot, codex, whatever you
[73:14] already trust in  and your rules
[73:18] come with it.
[73:19] And you put Open Claw on top of
[73:21] it.
[73:21] Persistent memory, heartbeats,
[73:23] and a claw right inside Slack or
[73:25] Teams.
[73:27] It's been really exciting to see
[73:31] Open Claw grow into something
[73:32] much bigger.
[73:35] A global movement.
[73:37] And a community.
[73:39] And I started the Open Claw
[73:43] Foundation, a really nonprofit.
[73:45] Any model, any operating system.
[73:48] Because we are entering a new
[73:51] era of building agents, more
[73:54] capability for the people who
[73:55] don't code, and more power for
[73:58] those who do.
[73:59] And we get to do this, we get to
[74:02] build it together in the open.
[74:04] So my task is simple:  Come
[74:07] build with us.
[74:08] Thank you.
[74:08] [Cheers and applause.]
[74:10] >> Fantastic.
[74:13] Thank you very much to Peter, to
[74:15] Samantha, to the companion app
[74:17] team for their hard work.
[74:18] Thank you to the Open Claw
[74:20] community for giving everyone
[74:22] here a crucifix  crustacean -- a
[74:25] crustacean of their own.
[74:26] And that's the first time you've
[74:27] even Open Claw running on a
[74:31] Surface ultra.
[74:32] [Cheers and applause.]
[74:35] >> All right.
[74:36] Thank you very much, Samantha,
[74:38] Scott, and Peter.
[74:39] It's so wonderful to see Open
[74:42] Claw come to Windows.
[74:45] And have all of that capability
[74:48] in terms of the security and --
[74:50] and that comfort, to be able to
[74:53] have these long running agents
[74:54] and unmetered intelligence come
[74:56] together.
[74:57] So now, let's move from the
[75:00] Windows side on the edge to the
[75:02] cloud with Foundry briar briar,
[75:04] and we're building Foundry into
[75:08] a full application platform for
[75:10] the agent era.
[75:12] Right?
[75:12] Every era of platform shift,
[75:15] when you move to the cloud, we
[75:17] have the cloud stack in Foundry.
[75:24] We're excited about the Foundry
[75:26] hosted agent as a runtime for
[75:28] long running agents.
[75:29] You know, agents now have -- if
[75:32] you're building in Foundry
[75:35] hosted agents, you can have all
[75:36] the IQ layers.
[75:38] You have the tools.
[75:39] You have the durability and the
[75:42] memory and the state.
[75:44] You have your own sandbox.
[75:45] In fact, it's a super-fast
[75:49] sandbox that you can spin up.
[75:51] You can generate the rubrics.
[75:54] You can -- and the evals.
[75:56] You can in fact have all the
[75:58] safety and the guardrails around
[76:01] your agentic system.
[76:02] And in fact, even one of the
[76:03] coolest things in Foundry is
[76:04] it's a continuously improving
[76:06] loop, even.
[76:07] It's got that self-improvement
[76:09] loop built in.
[76:10] You build an agent that's
[76:12] continuously getting better.
[76:13] And I'm really also excited
[76:16] today to announce a partnership
[76:20] with Fireworks AI, bringing all
[76:23] of their open wave models to
[76:26] Foundry.
[76:26] That means giving you as
[76:28] developmenters more choices, as
[76:30] well as that great inference
[76:31] stack, to build the next
[76:32] generation of these agentic
[76:35] indications, with all the
[76:39] enterprise Rayls that Foundry
[76:41] has, as well as the governance
[76:42] that Foundry has.
[76:43] Really excited about that
[76:44] partnership.
[76:45] [Applause]
[76:48] And so now that brings us to the
[76:52] tools.
[76:53] Data itself is at the heart of
[76:54] all this.
[76:55] You know, in fact, Jensen spoke
[76:56] to this.
[76:57] GitHub is not just about the
[76:59] code repo.
[77:00] It's becoming the control plane
[77:03] for all the agents.
[77:04] And nearly everything we measure
[77:05] on GitHub, whether it's repo
[77:09] creation, PR activity, API
[77:11] usage, actions, all of this are
[77:14] going faster because of the
[77:16] agentic workflows.
[77:17] The new scale is driven by
[77:19] humans and agents collaborating
[77:22] together.
[77:22] And they're exposing too long
[77:26] -- tooling crass every form
[77:30] factor.
[77:30] We've seen tremendous growth in
[77:31] CLI, driven, you know, the
[77:33] approachability of the CLI form
[77:35] factor, it's allusion -- always
[77:37] been great to go to a termal.
[77:40] And now when combined with
[77:42] models and natural language, CLI
[77:43] is the thing that everything
[77:44] goes to.
[77:46] But end of the day, when you
[77:48] have hundreds of CLI's, it
[77:51] becomes pretty complicated.
[77:54] It doesn't scale.
[77:55] Especially the cognitive load
[77:57] that I have when you have 100CLI
[77:59] sessions open, is such that you
[78:01] kind of need something new.
[78:03] And so that's what has led us to
[78:06] build -- you know, we need like
[78:08] this tool, essentially, that has
[78:11] the speed and the flexibility of
[78:13] a CLI.
[78:14] But has the capability of an IE,
[78:17] and the ability to scale to
[78:19] infinite number of agent
[78:21] sessions.
[78:21] So today we're taking that next
[78:23] big step, introducing our new
[78:27] git Hick Copilot app --
[78:29] Our new GitHub Copilot app.
[78:33] [Applause]
[78:34] And we realize it's not
[78:35] sufficient.
[78:35] Because you still have a back
[78:36] toned deal with.
[78:38] Code is easy to generate, but
[78:39] what about the back end?
[78:40] You need to content with
[78:43] identities, storage, database
[78:44] schemas, and that's why we're
[78:47] really super excited about
[78:51] Rayfin, an agent-first SDK that
[78:55] connects your agents to the back
[78:56] end as a service, and we're
[78:57] bringing this to everywhere you
[79:00] build.
[79:00] That's why I'm, again, super
[79:02] excited about Rayfin and the
[79:05] relationship with Replit.
[79:06] You can now build apps in
[79:09] Replit, while the -- yeah.
[79:10] [Applause]
[79:12] You can build the app in Replit
[79:14] while the app and data are
[79:16] deployed into the
[79:18] enterprise-managed fabric
[79:19] tenant, thanks to the Rayfin
[79:23] SDK, which is available now for
[79:26] anyone else to be able to use
[79:26] with their tools as a back end,
[79:28] and essentially have the back
[79:29] end service.
[79:31] And now to show you all of the
[79:34] Foundry and Rayfin and building
[79:36] agents and the long running
[79:38] agents, let me invite Cassidy.
[79:43] Take with away.
[79:44] [Applause]
[79:44] >> Hello, everyone!
[79:46] I'm so excited to be amongst my
[79:49] fellow Devs.
[79:50] This is a long day.
[79:51] Fix your shoulders.
[79:52] Sit back.
[79:52] I see a lot of you sitting up.
[79:54] Great.
[79:55] I know you're drinking from the
[79:56] fire hose of information today,
[79:57] so I want you to go into the
[79:59] next few minutes thinking, "What
[80:01] can I drywall try out on -- what
[80:04] can I try out later today?"
[80:06] First, I can't wait to show you
[80:08] the new GitHub Copilot app.
[80:10] This is your home base for
[80:12] development and operations on
[80:13] your computer.
[80:13] And we think you're going to
[80:15] love it.  Let me show you
[80:16] around.
[80:17] When you open up the app, you
[80:18] see the home screen where you
[80:20] can kick off a new session.
[80:23] But before night the serious
[80:26] stuff, you can drag Mona around,
[80:27] and there's a game!
[80:29] Look!
[80:29] It's so fun!
[80:30] [Laughter]
[80:31] I'm not very good at it, so
[80:32] let's just get back to, you can
[80:33] kick off a new agentic coding
[80:37] session.
[80:37] So I started off one a little
[80:38] bit earlier here.
[80:40] And it gave me a review of a
[80:42] bunch of release blockers.
[80:43] Which one should I fix?
[80:45] Call it out.
[80:48] 83?
[80:48] The critical ones?
[80:49] You know what?
[80:50] How about we just do all of
[80:51] them?
[80:52] Let's go!
[80:53] This app will now kick off a
[80:55] separate session for every
[80:57] single issue here.
[80:59] I don't have to worry about
[81:01] stashing or coding complex or
[81:02] anything, because the app takes
[81:03] care of that with Git work
[81:08] trees, that are isolated
[81:09] environments for each session.
[81:10] Your agents can work in parallel
[81:11] without stepping on each other.
[81:12] But you still have to merge
[81:15] them.
[81:15] Right?
[81:15] So -- [chuckling] -- Copilot has
[81:17] your back there too.
[81:18] If I head over -- not to this
[81:20] one.
[81:20] But to this issue here, I can
[81:23] run agent merge.
[81:24] And when I enable agent merge,
[81:27] cofight will baby sit this PR
[81:29] through CI check, code review,
[81:32] and merge collects.
[81:33] Okay.
[81:33] Let me keep showing you around
[81:35] while it's running.
[81:36] If I head over to my work, I can
[81:39] see a focused view of all of my
[81:41] activity ask just projects
[81:43] loaded in the app, PRs,
[81:45] everything here.
[81:46] And under automation, I have a
[81:47] bunch of reusable sessions that
[81:50] can run locally or on the cloud.
[81:52] You see there's issue poetry
[81:54] there.
[81:54] That's real.
[81:55] And that is load-bearing.
[81:57] Okay!
[81:57] [Chuckling].
[81:58] And now undersessions, like I
[82:00] briefly showed earlier, these
[82:02] are sessions.
[82:03] If I want to add a new
[82:06] repository, I can click the
[82:09] button, and it will pull one,
[82:11] and if I were to add one, I can
[82:12] add a session, and this is an
[82:15] open source -- I can start a
[82:19] session anywhere and it loads
[82:22] it.
[82:22] When I look at a session within
[82:24] this repository, let me look at
[82:26] the other one, I get an
[82:28] integrated browser, there's a
[82:31] terminal.
[82:31] And a chat.
[82:32] I can technology light and --
[82:33] toggle light and dark mode.
[82:35] And there's a button, pick and
[82:38] polish.
[82:38] I can polish anything in this
[82:40] app, and it adds it to the chat,
[82:42] and I can say, hey, I want you
[82:44] to add reordering to this list.
[82:46] And it will just work, all
[82:47] living in there.
[82:49] I have access to all the most
[82:50] popular models via my single
[82:52] GitHub Copilot subscription,
[82:55] including from open AI, Ann
[82:59] throw meic, and Google.
[83:00] And having a model change is
[83:02] agreed.
[83:02] Not only can you pick the right
[83:04] one, but Copilot can request a
[83:07] rubber duck review.
[83:08] In this session, I was use use
[83:12] GPT5.5, but it asked one from
[83:15] Claude opus 4.8.
[83:18] All models have blind spots, and
[83:20] the power of this approach means
[83:21] I can catch them earlier.
[83:23] This is all very cool.
[83:24] But working with AI in 2026
[83:28] should be more than just chat.
[83:29] You just saw me scrolling up.
[83:31] There's so many words here.
[83:33] [Laughter]
[83:33] So today, I'm very excited to
[83:35] show you the concept of a
[83:39] canvass.
[83:39] I'm going to own one right
[83:42] there.
[83:43] The canvas is how an agent can
[83:45] build a custom UI to communicate
[83:46] with you.
[83:47] What if your AI could see?
[83:51] Everyone say, demo gods, bless
[83:53] us.
[83:53] Let's see if it works.
[83:56] Here's a fun canvas where, if I
[83:58] get the camera going, okay.
[83:59] The agent shows your PRs down
[84:02] here, and I can toggle it with a
[84:04] thumbs up or thumbs down.
[84:08] Let's aapprove it!
[84:10] Hey!
[84:10] It's so fun!
[84:12] And goes deeper.
[84:12] This is just the beginning of
[84:13] what you can do.
[84:14] So again, I kicked off a bunch
[84:16] of different sessions earlier.
[84:19] This is a signal box app.
[84:22] It's 100% agent-build.
[84:24] It's containerized with a
[84:26] database back end.
[84:27] Would you be able to deploy this
[84:28] to your enterprise with no
[84:30] questions asked?
[84:31] Be honest.
[84:37] No.
[84:37] Yes.
[84:37] Exact.
[84:38] No, but you can with Rayfin, and
[84:39] that is very, very, exciting.
[84:42] Let me open up a new terminal.
[84:44] All I have to do is type "Rayfin
[84:48] up."
[84:48] And then, demo gods bless you!
[84:52] Come on!
[84:52] It will ...
[84:54] [Singing] maybe dedeploy!
[84:59] Blammo!
[84:59] It happened!
[85:00] Yes!
[85:00] And hosted on Microsoft fabric.
[85:02] I know that's a lot.
[85:03] I know that's a lot.
[85:04] With Rayfin, your agents get a
[85:07] complete enterprise back end, so
[85:09] you can deploy with confidence
[85:11] in the way that's best for you.
[85:12] But this is the key thing to
[85:14] remember:  This app is not just
[85:15] another session manager.
[85:16] Yes, it is.
[85:17] It managing a lot of sessions.
[85:20] But session managers just make
[85:22] it easy to create work, but this
[85:25] helps you to finish it.  Thank
[85:26] you very much.
[85:28] Happy pride.
[85:28] Back to you, Satya.
[85:31] [Applause]
[85:35] >> All right.
[85:36] Now we're back to IDEs that have
[85:40] UI.
[85:40] That sort of -- it's so cool!
[85:42] [Laughter]
[85:43] It's come full circle.
[85:45] Now, let's talk about how you
[85:48] can observe, govern, and secure
[85:51] these agents.
[85:53] Agent 365 is the agent control
[85:57] plane, agent requires their own
[86:00] identities, access controls.
[86:02] Even when they're working on
[86:03] your behalf, right?  You just
[86:05] want that work on behalf
[86:07] identity to be enforced.
[86:08] So we extended an intraagent
[86:14] need real-time defense, so we
[86:16] extended Defender.
[86:18] Agents require this always-on
[86:20] data protection and compliance,
[86:22] so we extended Purview.
[86:25] And these agents can be hosted
[86:27] everywhere, AWS, GCP, not just
[86:29] on Azure.
[86:30] Or build with any framework.
[86:33] And today we are announcing a
[86:36] number of updates, including the
[86:37] GA of agent 265SDK, and we're
[86:42] expanding it to your local
[86:44] agents running on Windows and
[86:45] elsewhere, and the claws, you
[86:47] just saw earlier.
[86:47] And let us take a look at how
[86:50] all the Agent 365 composes.
[86:54] Over to you, Amanda.
[86:56] [Applause]
[87:00] >> Everyone is building agents,
[87:01] but that's not the hard part
[87:03] anymore.
[87:03] The hard part is intrigue
[87:05] grating them into your business
[87:07] and governing them at scale.
[87:09] Today I will show you how
[87:10] Foundry makes this easy.
[87:11] Let's start locally.
[87:12] I've already built a line graph
[87:14] agent, and now I will show you
[87:16] the value Foundry adds.
[87:18] First, tools.
[87:21] Agents need tools to get work
[87:22] done, and with Foundry tool box,
[87:24] I just add my tools once, and
[87:25] any agent can consume them
[87:27] through a single MCP end point.
[87:31] And because tools essentially,
[87:33] that means governance does as
[87:35] well.
[87:35] I've applied a guard rile that
[87:38] blocks PII from leaking, and all
[87:42] I have to do is apply this once,
[87:44] and all my agents are protected.
[87:47] Now let's make this agent
[87:50] enterprise-ready.
[87:50] To deploy this agent to Foundry,
[87:52] all I have to do is add this one
[87:54] block of code, and then I push
[87:56] my changes and GitHub actions
[87:58] takes it from there.
[88:00] Once deployed, I can actually
[88:02] use this agent in the same
[88:04] Foundry extension I showed you
[88:06] earlier.
[88:07] And let's now test it out.
[88:09] I'm asking it to track a few
[88:11] open items, from a standup I had
[88:13] earlier today.
[88:14] And what's actually happening
[88:15] right now, is Foundry is
[88:18] spinning up a dedicated microVM
[88:20] just for this session.
[88:21] And the session even gets its
[88:24] own persistent file system.
[88:26] What you're going to see in a
[88:27] minute here, if I go to the
[88:29] Files tab to track the open
[88:32] items, the agent's actually
[88:34] writing to a file.
[88:36] Pretty cool, right?
[88:42] Off the top of my head has
[88:44] server side traces and evals to
[88:46] show me exactly what happened on
[88:47] every run.
[88:48] But how do I know if my agent's
[88:50] doing a good job?
[88:51] That's what Foundry's brand-new
[88:56] rubric evaluators are for.
[88:59] One easy command, Foundry reads
[89:02] my agent and generates the
[89:04] evaluation criteria for me.
[89:06] It creates a rubric personalized
[89:08] just to this agent.
[89:09] Now let's check out this rubric
[89:12] in Foundry portal.
[89:15] Look at these dimensions.
[89:17] Governance outcome correctness,
[89:20] prescribed source usage.
[89:21] I didn't write any of these.
[89:22] Foundry generated them from
[89:24] production traces.
[89:25] And with this rubric, I can
[89:28] score my agent, and run evals,
[89:30] but we can do so much more than
[89:33] that.
[89:33] That's where Foundry's brand-new
[89:35] agent optimizer takes over.
[89:37] Here's how it works.
[89:38] It tunes four things:  The
[89:41] model, instructions, tool
[89:43] descriptions, and scales.
[89:44] And then it generates improved
[89:46] candidates and scores each one
[89:47] using the rubric I just showed
[89:50] you.
[89:51] Here, I can view the candidates.
[89:52] I can see the strategy used.
[89:53] I can see their scores.
[89:55] And I can actually view exactly
[89:57] what changed.
[89:59] This candidate, for example,
[90:00] improved its score by updating
[90:02] the model and the system prompt.
[90:05] Foundry then makes it super easy
[90:07] to deploy the best candidate as
[90:08] a brand-new agent version.
[90:11] But this is not a one time
[90:14] thing.
[90:14] Every run feeds the next eval.
[90:16] And every eval tells the
[90:19] optimizer where to improve next.
[90:20] So your agents now get better
[90:22] the more they're used.
[90:24] But now, let's put this agent to
[90:27] work.
[90:27] I've published my agent to Teams
[90:30] and M365 Copilot.
[90:33] Right now I'm going to ask it to
[90:34] just catch me up on what I've
[90:36] missed, because I've been o
[90:39] offline all morning, but my
[90:40] agent hasn't.
[90:41] While it works on that, though,
[90:44] let me explain what makes this
[90:46] agent different.
[90:47] This is an autopilot agent,
[90:49] which means it has its own
[90:50] identity and productivity
[90:51] license.
[90:52] So it can work across M365 on
[90:54] its own behalf.
[90:56] Earlier, you saw me using this
[90:57] agent by myself.
[91:00] But shipping of features and new
[91:02] release takes a whole team.
[91:04] And that why this agent lives in
[91:05] our Teams group chat.
[91:07] Let's check out how it did.
[91:10] Serums as you didded --
[91:13] summariesed all updates and call
[91:15] out features I need to track.
[91:17] But putting this to work in your
[91:20] enterprise, governance needs to
[91:22] come first.
[91:22] That's why every autopilot agent
[91:27] requires admin approval to she
[91:31] can review it and choose who has
[91:32] the ability to talk to it.
[91:33] But it doesn'tent there.
[91:37] An over approval, add minutes
[91:39] anterior and block -- monitor it
[91:41] and block it at any time.
[91:44] But governance can't apply to
[91:45] just one agent.
[91:45] Every agent in your organization
[91:47] needs to be managed with the
[91:49] same rigor as users, apps, and
[91:53] devices.
[91:53] And that's exactly what
[91:54] Microsoft Agent 365 provides.
[91:57] So let's zoom out.
[92:00] Today we saw Foundry accelerate
[92:01] development, take your agent
[92:02] from local to enterprise-ready,
[92:04] and put it to work in M365.
[92:07] Foundry makes it simple.
[92:09] You build the agent; we handle
[92:10] the rest.
[92:11] Now back to you, Satya!
[92:13] [Applause]
[92:15] >> Thank you, Amanda.
[92:19] You know, what you just saw was
[92:21] how we're building security for
[92:26] AI.
[92:26] But there's also one other
[92:27] critical aspect, especially, you
[92:29] know, the news today is all
[92:32] about, you know, how do you
[92:35] defend yourself using AI against
[92:37] attacks that may in fact be
[92:39] using AI?
[92:40] Right?
[92:40] So last month, we announced our
[92:46] multimodel agentic security
[92:50] system in dash, an agent harness
[92:51] for security, essentially, that
[92:53] we build.
[92:53] We're bringing together a
[92:56] hundred agents across the
[92:59] frontier, and custom models, to
[93:01] really find these exploitable
[93:03] bugs better than any single
[93:05] model does.
[93:05] In fact, when we Dudebuted this
[93:10] harness, it was on top of cyber
[93:12] gym [dog barking]  benchmark --
[93:15] Jim benchmark.
[93:16] So look what you can do to
[93:18] defend against AI attacks.
[93:20] Over to you, Sarah.
[93:22] [Applause]
[93:29] >> Thank you.
[93:30] We know that security scans can
[93:32] take a while, so I'm not going
[93:33] to do one life.
[93:34] Let me show you the results of
[93:36] an ender scan I already ran on
[93:38] my code base.
[93:39] The system runs as a stand-alone
[93:41] CLI, but today, I'm using it in
[93:43] my GitHub Copilot app on my
[93:47] local dev machine.
[93:48] The scan is broken down but
[93:51] vulnerability, demands, and
[93:52] severity.
[93:53] And it's also an additional
[93:57] to -- finding traditional issues
[93:59] like coding errors and
[94:01] hard-coated secrets.
[94:03] It's identifying AI-specific
[94:06] vulnerabilities in the code
[94:06] base.
[94:07] What happened under the hood,
[94:08] over 100 specialized agents
[94:10] working together to discover,
[94:13] debate, and improve exploitable
[94:16] vulnerabilities end to end.
[94:17] When the scan finishes, it
[94:20] generates both a log and an HTML
[94:24] report that I can give to my
[94:25] management.
[94:26] The defender details command
[94:28] allows me to dig into these
[94:31] vulnerabilities, and you can see
[94:32] what it is, where it is in my
[94:35] code, and the severity to help
[94:38] me prioritize.
[94:38] And from here, of course, we are
[94:40] going to fix it.
[94:43] Now, using the Defender fix
[94:46] command, the system will
[94:47] remediate suggest the fixing
[94:48] directly in my local dev
[94:51] environment.
[94:52] When that's done, I can check
[94:52] out the diff.
[94:54] So I have full transparency
[94:55] about what the harness has done,
[94:57] and I still have a human in the
[94:59] loop check.
[95:02] But of course, everything I've
[95:03] shown you so far has run
[95:05] locally.
[95:06] But I can also create a PR to
[95:09] plug into my existing workflows
[95:13] and push up to my repo, and I
[95:15] can take the output from the
[95:17] scan and upload it to tools,
[95:19] like GitHub advanced security,
[95:21] and manage everything alongside
[95:22] my other indication security
[95:28] findings.
[95:28] I've shown it working on my
[95:29] code, but let me show you a
[95:31] vulnerability our security
[95:33] research teams identified using
[95:35] MDASH that you can look up
[95:38] yourself.
[95:38] So the TL;DR, because this is a
[95:40] lot to read of this bug, is the
[95:43] time reads an out of date map of
[95:45] an object.
[95:47] Runs off the end.
[95:48] And then crashes the host.
[95:50] So this is exactly the kind of
[95:53] bug that the harness was build
[95:55] for.
[95:55] Because the floor spread across
[95:57] three different parts of the
[95:58] code base.
[95:59] So no single file looks wrong on
[96:01] its own.
[96:03] They look absolutely fine.
[96:06] And we can even see here be
[96:10] probably my favorite part, a
[96:12] statement from the developers
[96:14] claiming everything is fine.
[96:16] This is exactly the sort of
[96:17] reassurance that fools normal
[96:19] scanners and single AI models.
[96:22] But MDASH wasn't fooled.
[96:24] One team of agents spotted the
[96:26] suspicious gap.
[96:27] Another team argue they it in
[96:29] part.
[96:29] And a third team build a working
[96:31] example that actually triggered
[96:33] the crash.
[96:34] And it did all of this in open
[96:35] source code base.
[96:36] And this is a kind of joined-up
[96:38] reasoning that previously
[96:41] required significant manual
[96:43] security research effort.
[96:44] This is MDASH helping developers
[96:46] create secure code from the
[96:48] start, coming soon to your CLI
[96:50] and the Microsoft Defender
[96:52] portal.
[96:52] Back to you, Satya!
[96:55] [Applause]
[96:56] >> Thank you, Sarah.
[96:58] So that was the stack.
[97:00] Before, though, we move to, you
[97:03] know, unpacking more of the
[97:05] opportunity, I want to do
[97:06] something different.
[97:07] I want to introduce two people
[97:10] whose LinkedIn profiles were
[97:12] both super impressive and
[97:14] slightly perplexing.
[97:18] Under role had says they're
[97:20] general partners on Mantis.
[97:22] On the previous role, sold out
[97:25] Madison Square Garden.
[97:29] Welcome Alex and Drew from the
[97:38] Chainsmokers.
[97:38] [Music]
[97:39] Thank you so much.
[97:40] One of the things when my team
[97:41] came to me and said, hey, we're
[97:43] going to have Chainsmokers at
[97:46] Build, I thought maybe that's
[97:47] what we're calling our new
[97:49] GitHub Copilot app.
[97:51] But maybe they're your fans.
[97:53] But tell me about how you got
[97:54] into this.
[97:55] I mean, you're being added for
[97:57] what -- at it for what, 12-plus
[98:01] years as investors first, and
[98:02] you had your firm now for
[98:04] seven-plus years, and you even
[98:05] picked what I would have not
[98:06] thought is the natural place,
[98:10] which is B2B SaaS.
[98:13] Give us the back story.
[98:15] >> I'm sure you guys are
[98:16] wondering what time line you're
[98:18] on, where we're at Microsoft
[98:20] Build, but hey, how are you.
[98:24] We've been in the Chainsmokers
[98:26] for 14 years, and you know, when
[98:28] our music started to take off in
[98:30] the mid two thousand teens, we
[98:31] got to play a few events like
[98:35] this.
[98:35] And we met a lot of the founders
[98:36] from the consumer mobile cloud
[98:38] era of startups.
[98:40] And they taught us a lot and
[98:41] give us a front row seat into
[98:45] what early stage investing was
[98:48] like.
[98:48] We fell love with it, had a lot
[98:50] in common with the founders and
[98:51] the way they build their
[98:52] business was the same way that
[98:54] we thought about starting the
[98:57] Chainsmokers and breaking
[98:58] through the noise there.
[98:59] And we got to participate in a
[99:01] few of their deals.
[99:03] And we decided to
[99:06] institutionalized in 2020 and
[99:07] start our own fund
[99:08] >> That's cool.
[99:09] When you hear about all the AI
[99:11] stuff and what's happening,
[99:12] there's a lot going on, even in
[99:14] your portfolio we were talking
[99:15] back staining.
[99:16] Things are changing.
[99:17] How do you see the opportunity
[99:18] going forward?
[99:19] >> I mean, there's so many
[99:21] vectors you could talk about.
[99:22] There's the creative output
[99:25] side --
[99:25] >> Yep.
[99:26] >> -- we've been experimenting
[99:27] with in music.
[99:28] But always important to have
[99:30] your authenticity when it comes
[99:31] to creativity.
[99:31] But on the invest side, we're
[99:33] moving from producing outputs to
[99:35] producing actions.
[99:36] Which I think presents a very
[99:38] interesting opportunity to
[99:40] reimagine the entire
[99:42] architecture of the way software
[99:43] enterprise is build.
[99:45] Instead of humus producing
[99:47] outputs -- humans producing
[99:49] outputs, it's machines, and
[99:50] rethinking the entire space in
[99:51] that context.
[99:52] >> It's fantastic to see that.
[99:56] And maybe to close out, any sort
[99:58] of -- as artists who have had
[100:00] great success, when you look at
[100:02] a founder, what sort of is the
[100:05] advice you give them when you're
[100:08] creating, you know, you're
[100:09] bringing something new, and it's
[100:11] a creative process.
[100:13] So what is -- what are you
[100:14] looking for in founders?
[100:16] What is it that you're giving
[100:17] them as advice?
[100:18] >> Totally.
[100:19] There really are so many
[100:20] parallels.
[100:21] Maybe it seems like that; maybe
[100:22] it doesn't.
[100:22] But it's really difficult as
[100:24] artists to really find, I guess,
[100:26] what your sound s what's
[100:28] authentically you.
[100:29] What you can continue to do over
[100:31] and over, because with this much
[100:33] competition, you have to be
[100:34] moving authentically.
[100:35] You have to be connected to the
[100:37] product that you're creating.
[100:39] And -- 'cause you're going to
[100:42] have to iteratedate and keep it
[100:44] going for a long time to lock
[100:46] into your fan base and make
[100:48] something special and nearby.
[100:49] And we try -- and unique.
[100:51] And we try to advise the same
[100:54] thing.
[100:54] >> Are you game for playing for
[100:55] us this evening for our closest
[100:57] friends here?
[100:58] >> We'll be there.
[100:59] >> 6:00!
[101:00] 6 this evening.
[101:01] Thank you very much.
[101:02] [Cheers and applause.]
[101:03] Thank you, thank you.
[101:08] We will be right here at 6 p.m.
[101:11] tonight, and really looking
[101:13] forward to it.
[101:15] So that effectively sort of
[101:17] talks a little bit about the
[101:19] developer stacks.
[101:19] So now let's talk about what is
[101:23] the opportunity for every
[101:26] company.
[101:26] Right?
[101:26] At the end of the day, we are an
[101:28] institution, an organizational
[101:30] builders, whether it's AI nave
[101:32] company, whether it's a SaaS
[101:35] company, or whether enterprise,
[101:36] the first thing we want to do is
[101:38] make sure that as you build
[101:40] things black --
[101:43] Build things like plugins or
[101:45] agents or AI apping, we want
[101:49] them to be discovered through
[101:51] the Microsoft ecosystem.
[101:52] That's job one for us.
[101:54] That's why we're doing things in
[101:55] Windows or Microsoft 365 Copilot
[101:57] or in Teams or GitHub.
[101:59] We want to structure them that
[102:03] your applications, agents,
[102:05] plugins are discovered.
[102:07] Customers are also building lots
[102:10] of line of business applications
[102:12] and agents using Copilot Studio,
[102:14] and we want to make that all
[102:16] discoverable again, as part of
[102:17] the Copilot experience.
[102:19] And Teams in some sense has
[102:22] become this destination for
[102:24] multiplayer, human to agent
[102:26] interaction.
[102:27] We want you to be able to find
[102:29] agents, interact with agents,
[102:32] right in Teams.
[102:33] And we are super-charging all of
[102:37] this.
[102:37] Copilot continues to evolve very
[102:40] quickly.
[102:41] It started first with chat, with
[102:43] some of the best models, with
[102:45] great access to Work IQ.
[102:50] We didn't have the name, but
[102:50] that's where it started.
[102:53] Then came Cowork, a new way of
[102:55] working, and generating the
[102:57] stunning artifacts, and solve
[102:59] these multistep problems.
[103:01] Right?
[103:01] So you assign a multistep task
[103:04] to Cowork.
[103:05] You saw GitHub, which has
[103:07] continued to evolve as well.
[103:09] And now, as of, you know, come
[103:13] summer, you will be bringing
[103:16] coding to all knowledge work
[103:18] within one Copilot super app.
[103:20] Right?
[103:21] That's going to be really
[103:25] exciting, to see -- yep!
[103:27] [Applause]
[103:27] You will have chat, cowork, and
[103:28] code all in Copilot.
[103:31] But today, we're introducing
[103:34] something completely new.
[103:40] Autopilots.
[103:40] When you think of autopilots as
[103:43] enterprise-grade claws.
[103:45] These are autonomous,
[103:47] long-running agents with full
[103:50] enterprise compliance that run
[103:52] in your tenant.
[103:53] Autopilots can have a name,
[103:58] personality, custom connecters,
[104:00] context, and memory.
[104:01] And they're totally new way to
[104:03] reduce toil and get you back to
[104:05] what you love, to kick things
[104:07] off, the the first autopilot
[104:11] we're introducing is Scout.
[104:14] Let's take a look.
[104:16] [Music]
[105:11] >> All right.
[105:12] [Applause]
[105:14] As you can see, Scout works
[105:17] where you work, joining group
[105:18] chats and teams, handling
[105:21] threats in Outlook.
[105:22] Starting today for those of you
[105:24] on Copilot Frontier, you can try
[105:26] out Scout.
[105:27] And in the coming months, we
[105:28] will build this out to a
[105:31] complete digital team of
[105:32] autopilots, right inside of
[105:34] Copilot.
[105:34] Right?
[105:34] So you can go to the Copilot
[105:36] app.
[105:36] Scout is the one that comes by
[105:37] default.
[105:38] But you can build more of these
[105:41] autopilots.
[105:42] And so that's the future of what
[105:44] we think of as the Copilot
[105:46] ecosystem itself.
[105:47] And so far, we have talked a lot
[105:50] about what you can do to build
[105:52] your own agents and how these
[105:53] agents are discovered and things
[105:54] like Copilot and Teams and
[105:57] Windows.
[105:58] But when you think about what
[105:59] makes any organization, any
[106:01] enterprise, any company unique,
[106:05] it is its Ttacit knowledge that
[106:09] is continuously compounding
[106:11] through its operations.
[106:12] So the key consideration at some
[106:14] level in an AI age is to ask the
[106:17] question, "What's the future of
[106:18] the firm?"
[106:21] How do you continue to preserve
[106:22] and compound that tacit
[106:24] knowledge in the age of AI,
[106:26] where models can learn anything?
[106:27] From the data and the
[106:29] trajectories they see.
[106:30] To do that, we believe that
[106:35] every organization, whether it's
[106:38] an AI native company, a SaaS
[106:41] company, or an enterprise, will
[106:42] need to build their own
[106:43] hill-climbing machine.
[106:45] It's a system that continuously
[106:49] improves against your
[106:51] objectives, your private evals,
[106:54] compounding your advantage over
[106:56] time.
[106:56] Not someone else's.
[106:59] To deliver on this, we're taking
[107:00] the next step with Frontier
[107:04] tuning.
[107:04] We've been working with many
[107:06] customers already to help, you
[107:08] know, really build their own
[107:10] learning loop.
[107:11] The environment, the context,
[107:13] the tools, the ribosomes -- the
[107:17] rubrics, and even train their
[107:18] own models.
[107:19] And today, we're really thrilled
[107:20] and excited about super charging
[107:23] that frontier tuning capability
[107:25] with the innovation that's
[107:26] coming out of our super
[107:28] intelligence lab, to tell you
[107:29] all about this, let me hand it
[107:31] over to Mustafa.
[107:35] [Applause]
[107:35] >> Thank you.
[107:38] [Music].
[107:39] Thank you, thank you.
[107:40] Thank you, good morning,
[107:41] everybody.
[107:43] You know, we really are living
[107:45] in the most remarkable times.
[107:47] Since I started working in AI,
[107:50] the computer that we used to
[107:53] train frontier models has
[107:55] increased by one trillion-fold.
[107:58] That is 12 orders of magnitude
[108:02] of computation in just 15 years.
[108:03] It's clear that a consistent,
[108:07] exponential increase in
[108:08] computation leads to predictable
[108:10] advances in AI capabilities.
[108:13] And in the next few years, we're
[108:15] going to see three more orders
[108:17] of magnitude of compute apply to
[108:25] train Frontier models.
[108:26] Long linear hill climbing is the
[108:28] norm.
[108:29] The scaling laws are clearly
[108:30] holding, and it is a remarkable
[108:32] time in our industry.
[108:35] And so in this context, we at
[108:38] MAI are building towards what we
[108:39] call humanist superintelligence.
[108:42] State of the art AI capabilities
[108:45] that are explicitly designed to
[108:47] serve people and organizations
[108:49] and not replace them.
[108:52] Because the type of AI that we
[108:54] create really does matter.
[108:57] We need an AI that places
[108:59] humanity first.
[109:01] But always prioritizes human
[109:05] well-being and human progress.
[109:07] This is the core philosophy and
[109:12] modification behind our efforts
[109:14] at Microsoft, and it shapes
[109:15] everything that we do.
[109:16] And as a platform company, our
[109:18] job and our commitment is to
[109:20] keep you developers building the
[109:23] absolute frontier.
[109:24] So today, we're very excited to
[109:27] announce a family of seven new
[109:29] models across image, voice,
[109:32] transcription, and coding.
[109:33] [Cheers and applause.]
[109:38] These are all built with real
[109:41] attention to detail and a
[109:42] commitment to making very
[109:44] practical and efficient tools
[109:45] that are typed to -- tuned to
[109:47] just how you work in the real
[109:49] world.
[109:50] First up:  MAI image 2.5 and its
[109:53] flash variant, two super strong
[109:55] models that deliver a step
[109:57] change in quality, not -- now at
[110:03] #2, surpassing the core of
[110:08] Nanabanana2.
[110:08] [Applause]
[110:09] They gave you precise editing
[110:11] with control and consistency,
[110:12] flashes here for super efficient
[110:15] production work loads, while 2.5
[110:17] gives you that maximum fidelity
[110:19] and professional grade
[110:21] performance.
[110:22] Alive in PowerPoint today,
[110:23] they're rolling out to One
[110:25] Drive, and you can access them
[110:27] on Foundry at a market leading
[110:29] quality per dollar.
[110:31] Next, MAI transcribe 1.5.
[110:34] This is the best transcription
[110:36] model in the world.
[110:37] State of the art accuracy across
[110:41] 43 languages, beating out Gemini
[110:44] and open AI's flagship models.
[110:47] We've optimized it for real
[110:48] world use so you can produce
[110:54] highly accurate triceps, five
[110:55] times -- transcripts, five times
[110:57] faster than all rival models.
[110:59] It's integrated inside of
[111:02] GitHub, Copilot, dynamic 365
[111:04] contact center, and available in
[111:05] Foundry where I'm excited to say
[111:08] it is the fastest, most
[111:11] official, and most -- efficient,
[111:12] and most cost-effective
[111:13] transcription model of any of
[111:14] the hyperscalers out there.
[111:15] [Applause]
[111:20] So paired with that, we've got
[111:22] MAI Voice 2.
[111:23] The latest speech generation.
[111:28] Beautiful prosody, natural
[111:29] delivery, fine grained control,
[111:34] in 15 languages with more to
[111:37] come.
[111:37] And voice 2 flash, providing the
[111:39] best value and speed for ultra
[111:45] lateliency voice, which is the
[111:46] giggest thing in 2026.
[111:48] Next up, text foundation model.
[111:50] MAI thinking one.
[111:51] This is the first reasoning
[111:53] model, and it's exceptionally
[111:55] strong, and our target use cases
[111:57] of reasoning and sweet tasks.
[112:00] It's a 35 billion active
[112:03] parameter E, meaning it competes
[112:06] in the medium sized weight
[112:10] class.
[112:10] And independent human raters on
[112:12] surge prefer it in overall
[112:13] quality side by side versus
[112:19] sonnet 4.6.
[112:20] [Cheering]
[112:21] It's achieved 97% on AMI2025,
[112:27] the key measure of its
[112:29] capabilities, but most
[112:30] importantly, it's now at 53% on
[112:34] bench pro that places it
[112:35] alongside opus 4-6, at least on
[112:39] the toughest coding benchmark
[112:42] out there.
[112:44] We're very happy with that.
[112:46] [Applause]
[112:46] There's plenty more for us as we
[112:49] get this into production and
[112:51] hill climb against real-world
[112:53] tasks and traffic, but what's
[112:55] most remarkable about this
[112:56] model, we think, it has climbed
[112:58] entirely from the bottom.
[113:00] And that means that it hasn't
[113:02] targeted any of the benchmarks
[113:07] specifically, with zero
[113:09] distillation.
[113:10] And to us, this is critical.
[113:11] It means that the model is
[113:12] created with an enterprise-grade
[113:14] clean and commercially licensed
[113:17] data lineage that means that you
[113:18] can put it into production in a
[113:21] very trustworthy way with
[113:22] complete confidence.
[113:24] Now, finally, I'm incredibly
[113:25] excited to announce MAI code 1
[113:29] flash.
[113:29] This is our new inference
[113:32] sufficient coding model tuned
[113:34] for VS code and GitHub CLI.
[113:42] 51%, despite five billion
[113:45] parameters.
[113:45] Close to haiku in terms of size
[113:47] but cheapner cost, delivering
[113:49] strong coding performance at
[113:51] great sufficiencily.
[113:53] And it's rolling out inside of
[113:56] VS Code.
[113:57] [Applause]
[114:00] Now, alongside distribution on
[114:02] Foundry and optimization for our
[114:04] 1P products, we're excited to
[114:05] make our models available on
[114:07] open router as well as Fireworks
[114:10] and base ten.
[114:11] This means for the first time
[114:12] you will be able to tune the
[114:14] weights directly yourself in an
[114:15] ecosystem of your choice.
[114:17] Now, across this entire family,
[114:19] safety and security has been
[114:21] build in from the start.
[114:23] Voice models come with
[114:26] protectiontion against
[114:27] unauthorized cloning.
[114:29] Everything is water marked from
[114:32] scratch.
[114:32] Reduced the reFOOUFLS,
[114:35] improvedish -- refuseals, and
[114:36] accomplishing a very detailed
[114:39] technical report today to give
[114:40] you a full and transparent
[114:41] understanding of how we put all
[114:43] of this together.
[114:46] One of the things I'm
[114:46] particularly excited about is
[114:48] that we have been carefully
[114:50] codesigning our models with our
[114:52] own silicon.
[114:53] That means that we've optimized
[114:55] MAI thinking one on our very own
[114:59] myo200 Chip and benchmarked
[115:04] against the GB2300.
[115:07] So top of the improvement, we're
[115:09] seeing a further 1.4X
[115:13] performance per Watt gain when
[115:15] we run them on the MIO model end
[115:18] to end, and that's huge.
[115:20] As everybody knows, this scale,
[115:23] every Watt counts.
[115:24] And silicon and model codesign
[115:26] is a really key advantage that
[115:28] we think is going to help keep
[115:31] everybody here right on the
[115:33] Frontier with the most efficient
[115:36] and powerful thinking and coding
[115:38] agents out there.
[115:38] And we're excited that these
[115:39] faster and more efficient MAI
[115:41] models are coming to the N1X
[115:43] that Satya mentioned a few
[115:45] moments ago, and we think that's
[115:46] going to be able to deliver the
[115:48] very best performance on mounds
[115:52] in a new -- on windows in a few
[115:54] months time.
[115:55] This is what owning the full end
[115:57] to end stack looks like.
[116:00] It lets you customize the MAI
[116:02] models using our full hill
[116:04] climbing machine right where you
[116:05] want it.
[116:05] And it means that the discipline
[116:07] and very relentless engineering
[116:10] that has gone into building our
[116:11] models is now available to all
[116:12] of you.
[116:13] On a platform that you can
[116:15] trust, working on your behalf,
[116:17] to create custom agents that you
[116:19] will control.
[116:21] So the really big thing, of
[116:23] course, that's happened in the
[116:24] last year is these RLEs.
[116:26] Reinforcement learning
[116:28] environments.
[116:28] These unique training gyms for
[116:29] your AIs.
[116:31] They create company and
[116:33] task-specific agents, adapted
[116:35] only to you, built on MAI
[116:38] models.
[116:38] For example, within Microsoft we
[116:40] use our RLEs combined with MAI
[116:42] models to climb towards the best
[116:47] agentic ace uses on Excel.
[116:48] The model is on par with GPT5.4
[116:52] on public and private
[116:55] benchmarks, and being ten times
[116:56] more efficient on cost.
[116:59] [Cheering]
[117:00] Thank -- [chuckling]
[117:00] And many other earlier adopters
[117:03] are seeing similar results.
[117:04] When we've tuned our models on
[117:07] McKensie's tasks, it delivered
[117:10] the highest win rate,
[117:12] outperforming GPT5.5, and
[117:14] delivering 10X grater efficiency
[117:16] on cost.
[117:16] To us, this is the advantage of
[117:18] very carefully calibrated
[117:21] frontier tuning.
[117:22] And importantly, unlike with
[117:25] some of the other companies,
[117:26] with  shared model
[117:35] only you keep benefits from the
[117:37] hard earned workflows, know how,
[117:39] knowledge and your own
[117:40] institutional data.
[117:41] Only you get to control the
[117:43] resulting model.
[117:44] So with us, the RLEs and the
[117:47] models that you build inside of
[117:50] them they become your mote.
[117:55] It marks a new era in AI that we
[117:57] are all really very excited
[117:59] about.
[118:00] Okay.
[118:00] So now just one final
[118:02] announcement that I am very
[118:03] excited about.
[118:04] We are taking customization and
[118:05] co creation of our models to the
[118:08] highest level possible on what I
[118:12] think offers the most important
[118:14] application of AI, healthcare.
[118:16] Today we are very proud to be
[118:18] announcing we are partnering
[118:19] with Mayo Clinic for a new
[118:24] frontier for health and deploy
[118:25] it in their hospitals and
[118:26] beyond.
[118:27] Help me in welcoming to the
[118:29] stage physician ground breaking
[118:30] researcher, president and CEO of
[118:33] the Mayo Clinic Dr. Gianrico
[118:42] Farrugia.
[118:47] [Applause]
[118:47] Thank you for being here.
[118:49] Everyone recognizes Mayo as the
[118:50] leading hospital in the world
[118:51] with incredible track record of
[118:54] research, innovation and
[118:55] clinical practice.
[118:56] Tell us more about what hope to
[119:01] get collaboration.
[119:03] >> Mayo Clinic is known for
[119:05] being able to live up to primary
[119:07] value the needs of the patient
[119:08] comes first.
[119:09] We deliver outstanding
[119:11] healthcare.
[119:11] We are ranked number one
[119:13] healthcare organization in the
[119:14] world, yet we know most people
[119:15] in the world will not have
[119:17] access to Mayo Clinic.
[119:19] So seven years ago we decided to
[119:21] create a platform, Mayo Clinic
[119:23] platform, moving all of
[119:25] healthcare from a pipeline to a
[119:29] platform.
[119:29] With our partners that platform
[119:31] is in four continents and
[119:33] reaches about 100 million
[119:35] people.
[119:35] It has created the largest, to
[119:38] our knowledge, longitudal
[119:43] healthcare center in the world,
[119:47] multimodal genomics.
[119:47] We have the opportunity to do
[119:48] what we do best together which
[119:50] is create a frontier model for
[119:52] healthcare.
[119:53] What it means if you are a
[119:55] patient, if you are somebody
[119:56] interested in healthcare, you
[119:59] can get clinical and logistical
[120:01] answers to your healthcare.
[120:02] If your healthcare provider,
[120:04] your physician it can give you
[120:07] insight, it can act as your
[120:10] real-time team member it can
[120:11] tell you what is likely to
[120:14] happen next, but it can also
[120:15] prevent harm and therefore
[120:17] increase patient safety and give
[120:18] valuable insights that make the
[120:20] team better at giving you what
[120:22] you need most which is better
[120:24] healthcare.
[120:24] >> One of the things we are most
[120:26] excited about is the models are
[120:28] already pretty incredible about
[120:31] textbook knowledge.
[120:32] Read all of the journals and
[120:34] papers.
[120:34] What they are looking is
[120:35] clinical practice and clinical
[120:38] expertise of your team and
[120:39] clinicians you developed over
[120:41] the last many decades.
[120:42] How do you think you might go
[120:44] about the clinical practice for
[120:47] the model and production?
[120:48] >> The exciting part here is we
[120:50] each do what we do best.
[120:52] We can tackle something that
[120:54] Hazel lewded healthcare for a
[120:56] long time trusted scalable
[120:58] solutions.
[120:58] To do that you need to have the
[121:00] right data, certainly need to
[121:02] have the right people, but you
[121:03] need to have a very patient
[121:06] focused lens.
[121:07] Between the two of us we have
[121:08] all of this together so we can
[121:10] build this frontier model and
[121:14] offer safe, secure, trustworthy
[121:16] and effective healthcare
[121:19] solutions for all.
[121:20] >> Number one objective is put
[121:21] the patient first, deliver high
[121:23] quality that we can as trusted
[121:25] as we can and share the world
[121:28] over.
[121:28] Excited to share this
[121:29] partnership.
[121:30] Can't wait to share more in the
[121:31] future.
[121:31] >> Us too.
[121:34] Thank you.
[121:36] [Applause]
[121:37] >> So today marks some very,
[121:38] very exciting steps that we are
[121:40] taking on our journey to create
[121:43] humanist super intelligence at
[121:45] Microsoft.
[121:46] We have an incredible roster of
[121:48] seven new world class models to
[121:49] keep everybody working at the
[121:50] absolute frontier.
[121:51] We are really looking forward to
[121:54] everybody being able to co
[121:55] create your own unique agents
[121:58] adapted to you at your control.
[122:01] I feel this is a new era in AI.
[122:03] An era of AI that you control on
[122:05] your terms.
[122:06] Let's build it together.
[122:08] Thank you very much everyone.
[122:17] Now over to Tanaya to show us
[122:20] how frontier learning works in
[122:23] practice.
[122:25] [Applause]
[122:28] >> Thanks Mustafa.
[122:31] With frontier tuning we are
[122:33] making it possible for you to
[122:34] create your own enterprise AI,
[122:37] with the models and the harness
[122:39] tuned on your data and your
[122:42] workflows.
[122:42] Let me show you how you can
[122:43] build your own health planning
[122:49] machine.
[122:49] MAI thinking one is available in
[122:51] private preview in the foundry
[122:54] model catalog.
[122:54] You can go ahead and deploy the
[122:59] m
[123:01] model as is or if you want to go
[123:03] on your health journey click the
[123:05] fine tune button.
[123:07] The first thing I will set.
[123:13] Next I am going to add a greater
[123:15] and that's it.
[123:16] I go ahead andee ate a  -- and
[123:20] create a job.
[123:22] It has through logs, scores them
[123:24] and we start the hill climb.
[123:28] If you want full control over
[123:30] the training loop let me give
[123:32] you sneak peak into low level
[123:34] training API.
[123:35] You can see I have the MEI
[123:38] sinking model.
[123:39] I can also configure my role out
[123:42] strategy as well as my hyper
[123:44] parameters to define exactly how
[123:47] I want my training algorithm to
[123:50] work.
[123:50] In fact I can also incorporate
[123:54] my own RLM by defining the tools
[123:57] this model interacts with.
[123:59] You just saw all of the code,
[124:02] but as M365 customer you never
[124:05] start from scratch.
[124:06] Let's hop on to Copilot.
[124:10] As a part of frontier tuning we
[124:11] are introducing a new way to
[124:15] build environments based on your
[124:18] data and your workflows.
[124:20] One of our customers land O
[124:24] lakes one of the largest
[124:25] businesses in the United States
[124:27] is using this to perfect that
[124:30] butter on your moerning --
[124:33] morning toast.
[124:34] Let me walk you through their
[124:36] environment.
[124:36] The environment has soft skills,
[124:39] knowledge and tools.
[124:41] But in the back end we create an
[124:44] entire origin for your agents to
[124:46] continuously learn the way that
[124:47] you work.
[124:50] Let's look at the butter report
[124:54] generation here.
[124:54] These tasks are very complex.
[124:56] They require many manual steps
[125:01] and a high degree of precision.
[125:02] In these tasks almost 80%
[125:05] accuracy isn't good enough.
[125:06] To hill climb to higher
[125:08] accuracy, we are extending the
[125:10] industry definition of skills to
[125:12] include Rubriks on what good
[125:17] looks like.
[125:18] This is one task.
[125:19] How do I scale this to qualify
[125:21] all
[125:22] all of the tasks in enterprise.
[125:24] You spend a lot of time in M365
[125:28] in teams, outlook, word, excel
[125:31] and PowerPoint.
[125:31] We use those signals to suggest
[125:33] skills and rubrics that define
[125:35] the way you work.
[125:38] Next you can add your
[125:40] organizational knowledge for
[125:40] branding like from OneDrive and
[125:43] SharePoint.
[125:43] The environment comes built in
[125:45] with Microsoft tools and you can
[125:47] add custom tools to it.
[125:50] Because these tools tap into
[125:52] real workflows we simulate
[125:57] execution so the model can learn
[125:59] without actually impacting the
[126:01] live state of your business.
[126:02] Now my most favorite part, the
[126:07] science.
[126:07] By generalizing all of these
[126:09] learnings into the main model as
[126:11] well as in the embedding model,
[126:14] we are able to hill climb for
[126:15] tasks that require high
[126:20] accuracy.
[126:21] And not only this, we are able
[126:25] to hill climb to more than 90%
[126:30] accuracy for Land-O-Lakes class
[126:32] for using the model.
[126:35] We estimate the model to be 10X
[126:38] efficient than the baseline
[126:45] models.
[126:46] Let's run the task.
[126:47] This time using this environment
[126:50] as an entrancing harness.
[126:53] Usually this task takes a couple
[126:54] of minutes to run.
[126:57] In the meantime I will show you
[126:59] a cashed response.
[127:08] They have research with multiple
[127:10] models including a fine tune
[127:12] model.
[127:12] What you see here is a summary
[127:15] that doesn't feel generic, it
[127:18] feels undoubtedly Land-O-Lakes.
[127:21] That's not it.
[127:21] The task holds itself to high
[127:25] standards.
[127:25] It continuously retrospect and
[127:31] evaluate success.
[127:32] With frontier tuning your agent
[127:35] continuously improves with
[127:37] environment on your data
[127:41] encoding the way you work.
[127:45] That is what I call frontier
[127:47] tuning as smooth as butter.
[127:53] We can't wait to see the
[127:57] environment you build.
[127:58] Back to you Satya.
[127:59] [Applause]
[128:00] >> Thank you very much.
[128:01] Thank you for the partnership
[128:08] here.
[128:09] What you saw is a pretty
[128:11] significant shift.
[128:14] Times come from just consuming
[128:17] frontier model to fully
[128:19] participating at the frontier
[128:20] and frontier ecosystem.
[128:25] That is the transition.
[128:28] You can have private evals and
[128:30] outcomes private RLEs and
[128:33] traces, your enterprise
[128:36] knowledge create scaffolding
[128:38] from models to hill climb.
[128:42] That will allow you to
[128:45] differentiate on-line what you
[128:49] control.
[128:50] There is a new operating point
[128:51] at the frontier.
[128:53] Where you can use a very
[128:57] effi
[128:59] efficient reasoning model and
[129:00] coding model and achieve
[129:04] frontier because you have done
[129:04] the hard work to create the RLE
[129:07] the hill climbing machine that
[129:10] create the model your hill
[129:13] traces can make to the frontier.
[129:16] We think the combination of the
[129:18] two is a game-changer and how
[129:19] people think about what does it
[129:21] mean to operate in the frontier.
[129:24] What does the token look like
[129:25] how are you in control what is
[129:27] the future of the firm those are
[129:29] the questions and really the
[129:31] ecosystem that gets built around
[129:33] as opposed to a few models
[129:36] hungry for all data.
[129:41] To close out I want to talk
[129:44] about the frontiers beyond to
[129:48] push the frontier what's the
[129:50] next best thing.
[129:57] Building the scientific
[129:59] discovery loop can have perhaps
[130:03] the biggest societal impact.
[130:06] Science today is a little too
[130:09] len
[130:10] lenient.
[130:10] You form a hypothesis, you run
[130:14] an experiment you wait for lab
[130:16] results and then you begin
[130:19] again.
[130:19] What if the scientific method
[130:21] itself can become more
[130:24] continuous, more program able.
[130:25] That's what we are doing with
[130:27] Microsoft discovery.
[130:29] Discovery brings together the
[130:30] models the ACP compute, the
[130:34] knowledge graphs of all of the
[130:36] scientific knowledge automated
[130:37] lab and simulation into one
[130:41] agentic discovery loop.
[130:45] I am thrilled to GA and
[130:47] Microsoft Discovery, to show you
[130:50] this in action let me invite
[130:51] David up on the stage.
[130:53] [Applause]
[130:54] >> Hello.
[130:54] I work in the Microsoft
[130:56] Discovery and quantum team.
[130:59] Today to recycle this plastic
[131:02] like this bottle you have to
[131:05] sled it and melt it.
[131:07] You cannot use it to make this
[131:09] bottle again.
[131:10] It is down cycling.
[131:13] We are using Microsoft discovery
[131:15] to advance that research.
[131:17] Let me show it to you.
[131:18] This is the Microsoft discovery
[131:22] app.
[131:22] It is VS code.
[131:24] Agentic Discovery has many with
[131:29] software engineers.
[131:30] I want to do three things as a
[131:34] scientist.
[131:34] I want to write a scientific
[131:36] paper about this topic.
[131:40] Instead of melting the plastic I
[131:43] can use proteins so you can
[131:45] recycle it again and again.
[131:48] Second I want to perform the
[131:50] actual discovery to define this.
[131:56] Third I want to create a
[131:57] protocol to test the results in
[132:01] a real lab.
[132:02] If you are a developer the steps
[132:04] will be very familiar to you.
[132:15] Let's launch it.
[132:16] This will is discovery engine.
[132:21] These are specialized agents
[132:23] always running in the background
[132:24] following the scientific
[132:26] methods.
[132:26] You can see the agents you can
[132:28] add more.
[132:28] Microsoft discovery includes a
[132:30] community of agents, models and
[132:34] tools across many domains.
[132:37] You can have officers fair
[132:39] parties or create your own.
[132:41] This is still running.
[132:42] It will be running for a while.
[132:44] Even hours or days.
[132:55] It is running simulations
[132:59] dynamically.
[132:59] You can see the files created
[133:02] here.
[133:03] This one is the research paper
[133:05] that I was asking for.
[133:08] Discovery using no less graph
[133:14] internally.
[133:15] It is internal knowledge.
[133:16] This is a critical asset because
[133:18] it provide compete visibility.
[133:23] Scientists can be in full
[133:34] control.
[133:35] How do you come up with the
[133:44] plastic.
[133:45] You need a way to generate the
[133:47] proteins and identify the most
[133:53] promising ones.
[134:00] There is no out of the box agent
[134:01] for that.
[134:02] That's okay.
[134:03] With Microsoft discovery there
[134:05] is not an agent for the task.
[134:07] You can create one on the fly
[134:08] which is pretty cool.
[134:10] Here is all of the files that it
[134:12] created.
[134:14] This one is the YAMMER for the
[134:19] agent.
[134:19] Now we need to generate
[134:21] candidates for the proteins.
[134:23] That task requires a lot of
[134:28] compute.
[134:29] It is integrated with HPC.
[134:31] You can use it.
[134:35] You can see the process.
[134:36] You start with C protein and
[134:38] created variations by replacing
[134:40] small segments.
[134:41] You can see the segments there.
[134:47] You apply to see if it helps or
[134:51] not.
[134:52] This is done millions of times
[134:56] exploring a huge tree of
[135:01] proteins.
[135:01] The result is 80 proteins sent
[135:03] to the lab for testing.
[135:06] It is like the software.
[135:08] Creating a protein is a little
[135:11] more complicated.
[135:16] The bacteria create the protein
[135:17] for me.
[135:18] So discovery created another
[135:20] file.
[135:24] This one here.
[135:26] This contains the DNA sequences.
[135:30] I can send them to the lab if I
[135:37] have a lab I can integrate it
[135:42] directly with the agent.
[135:47] Go to proteins and submit to
[135:48] lab.
[135:48] This will use a custom agent
[135:50] that sends instructions for the
[135:52] lab appointment.
[135:54] This is very real.
[135:56] That's an maut m automated lab.
[136:00] I have it right here.
[136:01] This is application controlling
[136:02] the lab.
[136:03] It has a Copilot interface so
[136:05] the scientist can interact with
[136:08] the lab to the science
[136:11] experiment.
[136:12] In this case Discovery has
[136:15] submitted the job.
[136:16] You can see it right here.
[136:19] This will take some time.
[136:20] Let me open a previous run.
[136:22] There you go.
[136:23] These are all of the steps that
[136:25] the agents are doing.
[136:26] Most of them completely
[136:28] automated with human
[136:30] supervision.
[136:31] How cool is that?
[136:32] [Applause]
[136:39] This is bringing the together
[136:41] the physical and agents in
[136:45] unified Discovery loop.
[136:47] This is one example of what
[136:50] Microsoft Discovery can do.
[136:51] Industries are using it today to
[136:54] embrace a new era of scientific
[136:57] discovery.
[136:57] Thank you.
[136:58] [Applause]
[136:59] Back to you Satya.
[137:00] >> Thank you so much David.
[137:02] Talking about scientific
[137:04] discovery, we are also
[137:05] continuing to make rapid
[137:12] progress on our long-term goal
[137:14] of building a scalable computer.
[137:15] We announced last year our first
[137:18] QPU.
[137:19] We created a new state of matter
[137:21] that was only theorized 100
[137:27] years ago.
[137:28] Proved it out that it exists.
[137:29] Our vision was to take a
[137:31] radically different approach to
[137:33] addressing the fundamental
[137:34] barriers to building a scalable
[137:37] quantum machine which is all
[137:38] about reliability, speed as well
[137:40] as size.
[137:41] Since then, we have continued to
[137:43] make progress across the full
[137:47] quantum stack with both our
[137:49] academic and industry partners.
[137:52] In Q north we will have a
[137:54] quantum computer powered by atom
[137:57] computers natural atom computers
[137:59] with our stack in there.
[138:00] We are working with algorithmic
[138:02] and Columbia and Zurich.
[138:05] We ourselves use this discovery
[138:08] agentic loop to accelerate the
[138:11] work in quantum compressing
[138:14] years of research into this last
[138:18] year.
[138:18] Today I am really thrilled to
[138:23] announce Majorana2.
[138:39] The resulting cube bits are
[138:42] reliable and maintaining their
[138:44] state much longer while other
[138:46] common approaches deliver a
[138:48] lifetime of micro seconds.
[138:52] Majorana 2 provide cubic
[138:55] lifetime of 20 seconds or up to
[138:57] even a minute.
[138:58] Essentially a thousand times
[139:00] higher than what we were able to
[139:02] achieve with high  -- my or  --
[139:11] Majorana 1.
[139:12] They are enabling complex
[139:14] quantum computation in that
[139:18] lifetime.
[139:18] All of this in the same cubic
[139:21] form factor of Majorana 1 in one
[139:25] 100th of a millimeter control of
[139:27] digital.
[139:28] Which again is a super important
[139:30] aspect.
[139:30] Making it all possible to fit a
[139:33] million of these cube bits in a
[139:35] chip smaller than a credit card.
[139:37] It is this combination of the
[139:39] reliability, the speed, the size
[139:41] that makes the topological
[139:44] approach so unique.
[139:46] With Majorana 1 we had proven
[139:48] out the foundational physics and
[139:52] with Majorana 2 we begin the
[139:54] engineering scale.
[139:55] [Applause]
[139:59] Yup.
[140:02] Ultimately it is never about
[140:04] tech for tech sake.
[140:07] It is about tackling those
[140:10] pressing challenges of people
[140:12] and planet.
[140:13] It is also the fundamental point
[140:15] of this conference.
[140:16] The question is not about
[140:18] whether you can build the next
[140:20] great model or platform even
[140:23] this quantum machine.
[140:24] It is the question of how do we
[140:26] build the frontier ecosystem
[140:29] together.
[140:30] Because there are really two
[140:32] stories people can tell about
[140:33] this moment.
[140:36] One is that technology
[140:37] concentrates power, reduces
[140:39] human agency and leaves the
[140:41] society to absorb the
[140:43] consequences.
[140:44] The other is that we use this
[140:46] next wave to unlock opportunity
[140:49] for developers, scientists,
[140:52] enterprises in every community.
[140:54] Our job is to make the second
[140:57] story true.
[140:58] That's our North Star for the
[141:01] frontier ecosystem.
[141:02] Let's all go build together.
[141:03] Thank you all very, very much.
[141:06] >> Thank you.
[141:24] [Music]
[141:59] >> Within reach.
[142:04] Quantum is our most fundamental
[142:07] physical theorem.
[142:12] For quantum to deliver real
[142:14] world impact it has to be both
[142:16] reliable and scalable.
[142:20] >> Reaching quantum at scale
[142:21] will allow us to make a more
[142:23] direct approach to solving
[142:25] problems in chemistry.
[142:26] >> Discovery in life sciences.
[142:30] >> We bring drugs to market it
[142:32] opens up millions of
[142:35] possibilities.
[142:35] Quantum will change the world.
[142:37] This change is just beginning to
[142:46] happen.