[00:24] [MUSIC PLAYING]
[02:04] [CHEERING]
[02:09] SPEAKER 1: Please welcome to the stage CEO
[02:12] of Google Cloud, Thomas Kurian.
[02:16] [UPBEAT MUSIC]
[02:34] THOMAS KURIAN: Hello.
[02:39] [CHEERING, APPLAUSE]
[02:43] THOMAS KURIAN: Welcome everyone to Google Cloud Next.
[02:46] Just one year ago, we stood on this same stage
[02:50] and promised a new future for AI.
[02:53] Today, that future is running in production at a scale
[02:58] that the world has never seen.
[03:00] Over the last year, we didn't just see adoption.
[03:05] We saw transformation.
[03:07] Nearly 75% of Google Cloud customers
[03:11] now leverage our AI products to power their businesses.
[03:15] We have thousands of agents and services across every industry,
[03:20] reaching billions of people through the global scale
[03:23] of our partner network.
[03:25] You have moved beyond the pilot.
[03:28] The experimenting phase is behind us.
[03:31] And now, the real challenge begins.
[03:34] How do you move AI into production
[03:37] across your entire enterprise?
[03:40] The answer is a unified stack.
[03:43] You cannot deliver AI by piecing together a puzzle piece
[03:47] of fragmented silicon and disconnected models.
[03:51] To drive real value, you need an architecture
[03:55] where chips are designed for the models,
[03:58] models are grounded in your data, agents and applications
[04:03] are built with models and secured by the infrastructure.
[04:09] Google uses this exact same OpenStack
[04:13] to reimagine how we serve users using our AI
[04:17] tools across Search, YouTube, Chrome, and Android.
[04:23] To take you inside that journey, let's hear from Sundar Pichai.
[04:28] [APPLAUSE]
[04:32] SUNDAR PICHAI: Thanks, Thomas, and hello, everyone.
[04:34] We are so glad you are here.
[04:37] The pace of technological change has
[04:39] been faster than I've ever seen, and I've been around technology
[04:43] for a while.
[04:44] In fact, this Sunday, I'm celebrating my Googleversary--
[04:49] 22 years, and I'm still feeling lucky.
[04:52] And I hope that luck extends to all of you in Vegas this week.
[04:56] I was drawn to Google all those years
[04:57] ago because of its ambitious mission.
[05:00] From the web to mobile to AI, every platform shift
[05:05] has given us new opportunities to advance our mission
[05:09] and to help our customers and partners achieve yours.
[05:13] As we move into the agentic era, we
[05:16] are taking this to the next level.
[05:18] We are making big investments now and for the future.
[05:22] In 2022, we were investing $31 billion in CapEx.
[05:27] This year, we plan to invest between $175 to $185 billion
[05:33] in total CapEx, a nearly 6x increase in just four years.
[05:38] And for 2026, just over half of our machine learning compute
[05:41] is expected to go towards the cloud business,
[05:44] so it will greatly benefit all of you.
[05:47] These investments are how we continue to stay at the frontier
[05:51] and ensure you're at the cutting edge as well.
[05:55] A big focus for us is to always be
[05:57] customer 0 for our own technologies,
[06:00] so we can be a better partner to all of you.
[06:03] Let me share a few examples of how we
[06:05] are rewiring our work with AI.
[06:08] First, coding-- we've been using AI to generate code internally
[06:12] at Google for a while.
[06:14] Today, nearly 75% of all new code at Google
[06:18] is AI-generated and approved by engineers,
[06:21] up from 50% last fall.
[06:23] We are now shifting to truly agentic workflows.
[06:27] Our engineers are orchestrating fully autonomous digital task
[06:31] forces, firing off agents, and accomplishing incredible things.
[06:37] As one example of how we are using agents,
[06:40] we recently executed a particularly complex code
[06:43] migration.
[06:45] Anyone familiar knows these migrations
[06:47] can take a while to get done.
[06:50] We created a system of agents taking on three different types
[06:53] of roles--
[06:54] planners, orchestrators, and coders.
[06:58] Working together with engineers, we completed the migration 6x
[07:01] faster than we could have a year ago.
[07:04] And now we are applying that approach
[07:06] across our entire development cycle,
[07:09] from experimentation to testing to evals.
[07:13] It's not just developers who are infusing AI
[07:15] into their workflows.
[07:17] Our marketing teams are fundamentally
[07:19] rethinking the creative process from concept to launch.
[07:23] Historically, adapting a campaign
[07:25] for every audience, channel, and country took weeks.
[07:28] For the recent launch of Gemini and Chrome,
[07:31] teams used our models to rapidly generate thousands of variations
[07:35] of our creative assets.
[07:37] This enabled personalization at a massive scale.
[07:41] It also led to 70% faster turnaround and a 20% increase
[07:45] in conversion.
[07:47] We are not only getting to market faster.
[07:49] We are also doing it more effectively with AI.
[07:52] Our security teams are also seeing significant gains
[07:55] with AI.
[07:57] Each month, our teams receive unstructured threat reports
[08:00] at a scale that would take thousands of hours to review--
[08:04] a nearly impossible task.
[08:07] Today, our Security Operations Center agents automatically
[08:11] triage tens of thousands of unstructured threat reports
[08:14] each month.
[08:15] By accelerating the extraction of critical intelligence
[08:18] and filtering out the noise, it's
[08:20] reduced threat mitigation time by over 90%
[08:24] We are more on the front foot than ever before.
[08:28] Those are just a few examples of what we are trying at Google.
[08:31] We are moving in a bold and responsible way.
[08:35] There's a lot of change happening.
[08:37] And it can feel like we are in the messy part of the innovation
[08:40] cycle.
[08:40] But we are starting to see the foundational building
[08:43] blocks coming together, unlocking
[08:45] a new wave of innovation.
[08:48] One thing that is super clear--
[08:50] we are firmly in the agentic Gemini era.
[08:53] Last fall, we introduced Gemini Enterprise as a front door
[08:57] to agentic AI in the workplace.
[09:00] What we have seen in just a few months since launch
[09:03] is how every employee in every organization
[09:06] can become a builder.
[09:08] This is an incredible shift.
[09:10] We are accomplishing bigger things faster,
[09:13] but this comes with complexity.
[09:15] The conversation has gone from can we build an agent to how
[09:19] do we manage thousands of them?
[09:22] Today, I'm excited to announce our new Gemini Enterprise Agent
[09:26] Platform.
[09:28] It provides the secure, full-stack connective tissue
[09:32] you need to build, scale, govern, and optimize
[09:35] your agents with confidence.
[09:38] Think of it as mission control for the agentic enterprise
[09:42] to help move your organization into the next phase
[09:46] of the agentic era.
[09:48] I'm going to turn it back over to Thomas to share more.
[09:51] But first, thanks again for being here.
[09:54] Together, we are building a blueprint for true business
[09:57] transformation, and I'm excited to see where it takes us next.
[10:01] Thomas, back to you.
[10:05] [UPBEAT MUSIC]
[10:20] THOMAS KURIAN: Thank you, Sundar.
[10:22] Today, we're taking the next step
[10:24] in bringing Google AI to every employee and every workflow.
[10:29] Gemini Enterprise is now the end-to-end system
[10:32] for the agentic era, the connective tissue
[10:35] between your data, your people, and your goals.
[10:38] It transforms disconnected processes
[10:40] into a single intelligent flow.
[10:43] This is our blueprint for the agentic enterprise.
[10:47] It's also the answer to that fundamental question.
[10:51] Intelligence plus automation must deliver value.
[10:56] To make this work, you need context and action.
[11:01] Intelligence comes from your data.
[11:04] Automation is driven by agents.
[11:06] To solve this equation at scale, you
[11:09] need a complete integrated system.
[11:13] Today, we will show you the layers of that system and all
[11:17] the innovations we're introducing at every layer,
[11:20] starting with the AI Hypercomputer,
[11:23] the purpose-built foundation optimized
[11:26] for the physics of the agentic era;
[11:29] the Agentic Data Cloud, the engine that provides agents
[11:34] with trusted business context; Agentic Defense, the autonomous
[11:40] protection that secures your entire AI lifecycle;
[11:45] the Agentic Platform, the system to build, deploy, and manage
[11:49] agents; and finally, the Agentic Task
[11:53] Force, the pre-built specialized agents that we
[11:57] offer that are ready to transform your business.
[12:02] Let's dive in.
[12:03] Now, the Gemini Enterprise Agent platform
[12:06] is the environment where your business logic, your data,
[12:10] and your models converge to drive autonomous action.
[12:14] It expands on the previous capabilities of Vertex AI
[12:17] and brings new capabilities to enable your teams
[12:22] to build, scale, govern, and optimize agents
[12:26] with the same architectural rigor you apply to your most
[12:30] mission-critical systems.
[12:32] At its core, we build on our support for state-of-the-art
[12:36] models.
[12:37] Our most advanced reasoning model, Gemini 3.1 Pro,
[12:41] is available in preview.
[12:42] It's optimized for complex workflow orchestration.
[12:46] It bridges the gap between strategy and autonomous
[12:48] execution, interacting with your APIs and systems
[12:52] with minimal tuning.
[12:54] Industry leaders, including Databricks, JetBrains,
[12:57] and Replit, have chosen Gemini 3.1 Pro.
[13:01] We're also announcing Gemini 3.1 Flash Image,
[13:06] also known as Nano Banana 2, for high-fidelity visual assets;
[13:11] Veo 3.1 Lite, our most cost-effective video model,
[13:16] designed to build high-volume video applications;
[13:20] and Lyria 3 Pro, a state-of-the-art model
[13:24] for enterprise and professional-grade audio
[13:28] and music.
[13:30] All of these models are available in preview.
[13:33] Finally, we support all the leading models from Anthropic,
[13:37] including Claude Opus, Sonnet, and Haiku.
[13:41] And today, we're adding support for Anthropic's Claude Opus 4.7.
[13:47] While all of these models represent a massive leap
[13:51] in intelligence, their true value
[13:53] is realized when they're operationalized to solve
[13:56] mission-critical problems.
[13:59] Earlier this year, we announced a monumental partnership
[14:03] with one of the world's most iconic brands,
[14:07] that will bring the power of our technology
[14:09] to users everywhere around the world.
[14:12] We're collaborating with Apple as their preferred cloud
[14:15] provider to develop the next generation of Apple foundation
[14:19] models based on Gemini technology.
[14:22] These models will help power future Apple Intelligence
[14:26] features, including a more personalized Siri coming
[14:31] later this year.
[14:33] Leading organizations aren't just
[14:35] using Gemini Enterprise to work faster.
[14:38] They're using it to redefine what their businesses can do.
[14:43] Citi Wealth, in partnership with Google Cloud and DeepMind,
[14:47] today unveiled Citi Sky, an always-on AI-powered member
[14:53] of the Citi Wealth team that brings
[14:56] Citi's global intelligence to clients' fingertips.
[15:03] Citi Sky will provide an exemplary experience,
[15:06] allowing clients and colleagues to ask
[15:09] more of Citi Wealth whenever they need it,
[15:12] and in multiple languages.
[15:15] Honeywell generates billions of insights for managing buildings
[15:20] by training digital twins on over a million product
[15:23] specifications.
[15:25] Liverpool is bringing their signature in-store
[15:29] service online, projecting a 10 times return on investment
[15:34] on their new shopping assistant.
[15:36] And for a mission that's quite literally out of this world,
[15:42] we were very proud to partner with NASA--
[15:45] [CHEERING, APPLAUSE]
[15:53] --to use Gemini Enterprise Agents to power flight readiness
[15:58] and ensure astronaut safety for Artemis II, which
[16:03] set the human spaceflight record for traveling
[16:06] the furthest distance from Earth.
[16:09] Now, we built the agent platform to manage the entire lifecycle
[16:13] of an agent.
[16:15] The low-code Agent Studio enables every employee
[16:18] to build and deploy agents using natural language.
[16:22] It grounds LLM reasoning in your specific business rules,
[16:27] bringing predictable, autonomous action
[16:30] to every workflow at scale.
[16:33] To manage these assets, the agent registry
[16:37] provides a single point of control,
[16:39] indexing every internal agent and tool
[16:42] across your organization to ensure they're
[16:44] discoverable and governed.
[16:47] Similarly, our Skills and Tools Registry
[16:51] allows you to define modular, reusable packages
[16:55] of instructions, scripts, and resources
[16:58] to teach agents to perform specialized tasks or repeatable
[17:02] workflows.
[17:04] We also expose skills for every GCP service
[17:09] and every service in Workspace in our skills registry.
[17:14] All of this is also supported by our Agent Marketplace,
[17:18] allowing you to search and deploy
[17:21] specialized agents from our global partner ecosystem
[17:25] directly in Gemini Enterprise, including Atlassian, Box,
[17:31] Lovable, Oracle, ServiceNow, Workday, and many, many more.
[17:38] Finally, with our native integration
[17:41] of Model Context Protocol, or MCP,
[17:44] you can connect our agent platform to any MCP server.
[17:49] We're also exposing all of our GCP services
[17:54] as MCP to allow you to interact seamlessly
[17:57] with GCP from any agent.
[18:01] In addition to developing this agent platform,
[18:04] it also provides the framework to orchestrate and scale
[18:08] your entire agentic workforce.
[18:11] We enable agent-to-agent orchestration,
[18:14] allowing agents to seamlessly delegate tasks from one
[18:18] to another, including support for complex generative
[18:24] and deterministic orchestration patterns.
[18:27] This helps ensure that for your critical workflows,
[18:31] such as those that need compliance,
[18:34] your agents can follow well-specified paths
[18:38] every single time, guaranteeing predictable outcomes.
[18:44] Orchestration flows and agents can respond to events.
[18:47] These events can be real-time.
[18:49] They can be schedule-based.
[18:51] They can be trigger-based, or they can be batch inference job.
[18:56] We're bringing zero-trust verification
[18:59] to every agent and every orchestration step.
[19:04] And now with Agent Identity, every agent
[19:09] has a unique cryptographic ID and well-defined authorization
[19:15] policies that are traceable and auditable,
[19:19] ensuring you can track every action in your company
[19:24] and manage all your agents.
[19:27] They're also centrally managed through our Agent Gateway, which
[19:32] provides a single command center for policy enforcement
[19:36] across the organization.
[19:38] Paired with Model Armor, you protect your models.
[19:42] You protect your proprietary enterprise data
[19:45] from threats such as sensitive data leakage.
[19:49] This integrated approach, from secure sandboxes
[19:54] to a single management console, provides the visibility
[19:57] and isolation you need to run your most-sensitive workloads
[20:03] with a high degree of confidence.
[20:06] Now, optimization and observability
[20:08] are also built into the fabric of the platform.
[20:12] Agent observability delivers granular instrumentation,
[20:16] allowing you to visualize the full execution
[20:19] path of any agent using OTel-compliant telemetry.
[20:25] You can retrieve traces, monitor tool use,
[20:28] quickly diagnose reasoning loops with fine-grained logging.
[20:33] All of these capabilities that we've just detailed--
[20:37] the orchestration, zero-trust security, and developer tools--
[20:42] form the core engine of Gemini Enterprise.
[20:46] While the agent platform is where your technical teams build
[20:49] and govern agent, the Gemini Enterprise application
[20:53] is the primary environment where your business actually operates.
[20:58] It's that new front door to AI for all your employees,
[21:02] turning complex agentic capabilities
[21:04] into a simple, new way to work for every employee.
[21:10] By unifying data across your entire stack,
[21:13] including Google Workspace, coding tools,
[21:17] and enterprise systems, we've created
[21:20] a single intelligent flow for the entire organization.
[21:24] To give your teams a permanent memory,
[21:28] we're introducing Gemini Enterprise projects.
[21:32] Projects give your agents a high-fidelity workspace
[21:36] and the ability to use deep think
[21:39] to solve your most complex business challenges
[21:42] without context pollution.
[21:45] We're also adding Microsoft 365 interoperability,
[21:48] allowing you to export the Docs and Slides you've
[21:51] created within Canvas into common Microsoft Office formats.
[21:56] [APPLAUSE]
[22:02] Leading innovators already use this platform
[22:05] to redefine their industries.
[22:07] Let's hear from one of them, Virgin Voyages.
[22:11] [STIRRING MUSIC]
[22:15] NIRMAL SAVERIMUTTU: Our people are a secret sauce.
[22:17] So anytime we can use data to make our people more empowered,
[22:21] we're all in.
[22:23] Gemini Enterprise is a front door for AI in our business,
[22:26] and we're using it to bring natural language intelligence
[22:29] to our crew.
[22:30] BILLY BOHAN CHINIQUE: Gemini Enterprise is helping us bring
[22:32] Project 3D to life.
[22:33] It's going to allow our crew to feel confident
[22:36] with any question they're asked.
[22:38] So for sailors, Rovey is a personal concierge
[22:41] to maximize the value they get out of their vacation,
[22:44] inspired at home all the way through the last day
[22:46] of their voyage to make sure they have the best
[22:48] time on vacation with us.
[22:50] Our crew are already world-renowned and award-winning
[22:53] for the exceptional service they deliver as humans.
[22:55] We want Project 3D and Gemini Enterprise to be able to take
[22:58] that even further.
[23:00] So you can imagine when you're building
[23:02] something for a cruise ship, there
[23:03] are challenges that pop up.
[23:05] There's not exactly a fiber cable running out
[23:07] the back of the ship.
[23:08] With Google Distributed Cloud Edge,
[23:10] we had data resiliency at the core of the project.
[23:13] And that means, even when we're offline,
[23:15] our intelligence is still online.
[23:17] Using our AI stack in Google Cloud has helped us
[23:21] not only reduce our production timeline by up to 60%,
[23:24] but we have contributed to a 28% increase month
[23:27] on month for a record sales quarter.
[23:31] We could truly see a day where you get on a Virgin Atlantic
[23:34] flight, stay at a Virgin hotel, and end up
[23:37] on a Virgin Voyage, all connected seamlessly by Project
[23:40] Ruby and Gemini Enterprise.
[23:41] NIRMAL SAVERIMUTTU: Google has been
[23:43] a foundational partner for us.
[23:44] The extension of Gemini.
[23:45] Enterprise was the natural way to bring that mission to life.
[23:53] [APPLAUSE]
[23:58] THOMAS KURIAN: The era of the pilot is over.
[24:00] The era of the agent is here.
[24:03] But the true power comes from how it changes your workflow.
[24:07] Let's see it in action.
[24:09] Please welcome Erika Chuong.
[24:11] [UPBEAT MUSIC]
[24:21] ERIKA CHUONG: Thanks, Thomas.
[24:23] Gemini Enterprise is a platform to build, manage,
[24:27] and interact with agents.
[24:28] I'll demonstrate how it can orchestrate multiple agents
[24:31] across platforms, share context for seamless handoffs,
[24:34] and streamline workflows.
[24:36] Imagine I work for a global furniture retailer.
[24:39] Here's my personalized homepage where
[24:41] I can interact with internal business
[24:43] context and external sources in a single pane of glass.
[24:49] The agent gallery hosts my approved selection of agents,
[24:52] including built-in ones by Google and my company's agents,
[24:55] like this one for price and margin optimization,
[24:58] which autonomously orchestrates across agents, tools,
[25:02] and sources.
[25:03] Now, let's see Gemini Enterprise in action.
[25:07] To bring some less popular product lines back to life,
[25:11] we'll ask our agent to analyze current interior design trends,
[25:14] identify dead stock in our warehouse,
[25:17] and orchestrate a relaunch campaign.
[25:20] With that one prompt, multiple agents
[25:22] complete a series of actions in minutes instead of hours.
[25:26] My market research agent, powered by Deep Research,
[25:29] analyzes the latest Google Search information
[25:32] alongside my own sales and CSAT data.
[25:36] My data insights agent is connecting to our global product
[25:39] catalog, regardless of location or format.
[25:43] It knows that dead stock refers to stale inventory,
[25:46] and it uses our Agentic Data Cloud
[25:48] to identify the right data sets.
[25:51] And my product strategy agent pulls everything together.
[25:55] I'll go ahead and approve this plan.
[25:57] This can take a few minutes, so we'll fast-forward.
[26:00] Here are the highlights.
[26:03] Organic Modern is a huge trend, and customers
[26:06] are paying top dollar.
[26:08] Our Tuscany collection has leftover inventory
[26:11] that actually fits right in, but sales haven't been great.
[26:17] The recommendation-- rebrand and reprice it
[26:20] above our current discount but below competitor pricing.
[26:25] All of that with one simple prompt.
[26:28] I can even see the sources that it analyzed
[26:30] to make these recommendations.
[26:33] Gemini Enterprise is also suggesting a new landing
[26:36] page and new media to put front and center.
[26:40] I'll ask my product strategy agent to generate some videos.
[26:47] This part can also take a little bit of time,
[26:49] so we'll look at some pre-generated options.
[26:56] This looks amazing.
[26:59] Veo 3.0 placed the exact pieces in a whole new organic living
[27:03] style space.
[27:05] Now, let's get that website updated.
[27:08] I'll ask my dev agent to coordinate with our engineering
[27:11] team.
[27:13] It connects directly with Jira to give a developer full context
[27:17] for a seamless handoff.
[27:20] And on the developer's end, I'll automatically
[27:23] get a notification right here in my Google Chat with that Jira
[27:27] ticket.
[27:28] I'll get this going with CLI.
[27:33] Here, I'll ask my dev agent to start
[27:35] working on that Jira ticket.
[27:37] It's going to give me an overview of the strategy,
[27:40] the assets, and how it plans to execute on this web page
[27:44] using all of the context from the previous Gemini Enterprise
[27:47] session, the ticket, and our brand and coding guidelines.
[27:52] This is really easy.
[27:53] Let's go ahead and get this built and deployed.
[27:56] From here, this can take a little bit of time--
[27:59] I'm sure the devs in the audience
[28:00] know that-- so I gave it a little bit of a head start.
[28:03] Let's check out that final product.
[28:07] This looks perfect.
[28:10] Now let's head back over to Gemini Enterprise
[28:13] to prepare our stores for the launch.
[28:16] Our store operations team would usually handle this piece,
[28:20] so I'm in a brand-new session.
[28:22] I'll ask for a deck to get our regional distributors up
[28:25] to speed.
[28:27] Gemini Enterprise uses my company's context
[28:30] to find exactly what I need to know about that launch, how
[28:33] I've managed these in the past, and my team's sales goals.
[28:37] Then it works with the Google Workspace agent
[28:39] to create a branded deck in my personal style.
[28:42] Again, this can take a minute or two,
[28:44] so we'll look at one I generated ahead of time.
[28:47] Get this.
[28:48] With our brand-new Canvas mode, I
[28:50] can make edits and collaborate in Google Slides
[28:53] without ever having to leave Gemini Enterprise.
[28:57] Overall, I think this looks great,
[28:59] but I actually prefer the word "reimagined" here.
[29:05] Perfect, and I'll go ahead and share this with my team.
[29:14] And it looks like some of them are already jumping in.
[29:17] Let's recap.
[29:18] We saw Gemini Enterprise orchestrate multiple agents
[29:21] with a single prompt, share context,
[29:24] and generate and collaborate content
[29:26] with Google Workspace, all supported
[29:29] by our enterprise-grade governance, which
[29:31] gives organizations the power to manage and secure agents
[29:35] at scale, all while maintaining control
[29:37] over their business data.
[29:39] That's the magic of the Gemini Enterprise Agent platform.
[29:44] [UPBEAT MUSIC, APPLAUSE]
[30:03] THOMAS KURIAN: Thank you, Erica.
[30:05] Thousands of companies around the world and across industries
[30:08] are choosing Gemini Enterprise to transform their business.
[30:12] We're seeing a profound shift.
[30:13] Companies aren't just redesigning workflows.
[30:16] They're turning their everyday employees into AI builders,
[30:20] empowering them to solve their own hardest problems.
[30:25] Signal Iduna is redefining insurance in Germany
[30:29] with Gemini Enterprise.
[30:31] They hit 80% adoption within weeks,
[30:34] making it their front door for AI.
[30:37] 11,000 employees are now building specialized agents.
[30:41] Their health agent automatically verifies coverage
[30:45] against a century of complex policy data,
[30:49] driving a 400% surge in weekly users
[30:53] and providing answers 37% faster.
[30:57] Bosch is adopting Gemini Enterprise globally.
[31:00] From finance to engineering, employees
[31:04] are deploying custom agents so that they can reclaim time
[31:08] for complex research.
[31:09] KPMG reached 90% adoption with over 100 agents
[31:14] in just the first month.
[31:17] The American Society for Clinical Oncology
[31:19] is now delivering cancer expertise faster.
[31:23] Merck is bringing Gemini Enterprise to 75,000 employees
[31:28] to support their purpose to save and improve lives.
[31:34] And Walmart is rolling out Gemini Enterprise--
[31:38] [APPLAUSE]
[31:41] Walmart is rolling out Gemini Enterprise
[31:43] to help their store leaders spend more time with customers.
[31:47] Let's take a look.
[31:52] KIERAN SHANAHAN: At Walmart, we're
[31:53] known for being people-led and tech-powered.
[31:56] It's how we deliver on our promise
[31:57] to help our customers save money and live better.
[32:02] But in a retail landscape that's moving faster than ever,
[32:05] there's one thing that's critical to our success--
[32:09] trust.
[32:10] It's the backbone of our relationship with our associates
[32:12] and customers.
[32:14] And our field leaders build trust
[32:15] by being present on the sales floor,
[32:18] not in their offices at a screen.
[32:20] So we're giving our associates the latest tech
[32:23] and tools at their fingertips so that they
[32:25] can spend more time on the floor with their teams,
[32:29] serving our customers.
[32:31] By combining our data with advanced AI technology,
[32:35] Google Cloud is helping our leaders make better decisions
[32:38] with clearer insights.
[32:40] Gemini Enterprise is helping us take an important step forward,
[32:44] helping our leaders lead and solving problems
[32:47] so we can serve our customers better.
[32:49] Our store and supply chain leaders
[32:51] now have a Pixel Fold connected to Walmart's enterprise data
[32:56] and giving them the answers they need in seconds, not hours.
[32:59] By putting people first and taking their hands,
[33:03] we're building a stronger future.
[33:05] Thanks to our partnership with Google Cloud,
[33:07] we continue to transform retail every day.
[33:11] I'm excited for what's next.
[33:15] [APPLAUSE]
[33:21] THOMAS KURIAN: This winter, ahead
[33:23] of one of the biggest moments in global sports,
[33:25] our world-class engineers collaborated
[33:28] with world-class athletes.
[33:30] We're incredibly proud of how Google Cloud is
[33:33] helping athletes push the boundaries of their sport
[33:37] with AI.
[33:41] SARAH KENNEDY: There are sports that
[33:43] have a limited amount of data that's captured.
[33:45] And I think that's a beautifully perfect challenge
[33:48] for a company like Google.
[33:49] SPEAKER 2: Let's roll it.
[33:50] SARAH KENNEDY: One of the really cool things that we're
[33:53] partnering with Team USA to do is
[33:54] to help them understand what's that edge that we can possibly
[33:57] give them?
[33:58] MICHAEL SANTORO: They understand what's happening in the moment
[34:00] better than anybody.
[34:01] KATHERINE LITINSKY: So to have their input and their say
[34:03] can really help us create something that's of benefit.
[34:06] SARAH KENNEDY: The side-by-side comparison thing
[34:08] I'm excited for.
[34:08] KATHERINE LITINSKY: Everything is running on Google Cloud.
[34:11] The 3D model that we're using is a collaboration with Google
[34:14] DeepMind.
[34:15] We've moved so far past talking to an LLM with text.
[34:18] SPEAKER 3: It's so clear as day right here.
[34:20] It's amazing.
[34:20] SARAH KENNEDY: Whoa.
[34:21] Is that me?
[34:22] That's crazy.
[34:23] Operating at the speed of sports is
[34:25] one of the greatest challenges that we could be helping solve.
[34:28] If we can do that for the Winter Olympics in Milan,
[34:31] we can do that for any industry all over the world.
[34:36] SPEAKER 1: Please welcome three-time Olympic gold
[34:39] medalist, entrepreneur, and snowboarding legend,
[34:42] Shaun White.
[34:44] [CHEERING, APPLAUSE]
[34:46] [ROCK MUSIC]
[34:49] SHAUN WHITE: What's up, Google Cloud Next?
[34:55] Oh, wow.
[34:59] Man, I love seeing it snow in the desert.
[35:02] This is wild.
[35:03] All right.
[35:04] Look, back when I was training, our tools
[35:07] were camcorders and basically guesswork.
[35:10] You'd land a trick and watch it back,
[35:12] and you'd be looking at it thinking, how can I
[35:14] make that trick better?
[35:16] Over time, I've seen the tricks get bigger
[35:18] and the walls get higher.
[35:20] And now, Google Cloud is bringing AI to the mountain.
[35:23] This winter, I worked with some crazy-smart Google Cloud
[35:26] engineers on some awesome tech.
[35:29] And to show you how rad this is, give it up for Jason Davenport.
[35:34] [APPLAUSE]
[35:36] [ROCK MUSIC]
[35:42] JASON DAVENPORT: What's up, Shaun?
[35:43] SHAUN WHITE: What have we got for everyone?
[35:45] JASON DAVENPORT: All right, Shaun.
[35:46] This is going to be super fun.
[35:48] Let's unleash the power of Google Cloud AI
[35:50] into the half-pipe.
[35:52] And to do that, we're going to pull up a trick from your 2017
[35:56] Burton US Open.
[35:57] SHAUN WHITE: Oh, wow.
[35:58] JASON DAVENPORT: I'm sure you remember this one.
[35:59] SHAUN WHITE: It's a throwback.
[36:00] It's a throwback.
[36:00] JASON DAVENPORT: It is.
[36:01] So let's analyze this clip for the audience.
[36:03] And so they can see all the cool things that AI sees.
[36:06] SHAUN WHITE: All right.
[36:06] JASON DAVENPORT: All right, let's start it off.
[36:08] First, this trick is over in under three seconds.
[36:12] You are literally-- it's a split-second blur.
[36:14] And for everyone watching, what trick is this?
[36:17] SHAUN WHITE: This is a switch cab double flip 1440.
[36:20] JASON DAVENPORT: That is insane.
[36:21] But for everyone else, what does that actually mean?
[36:25] SHAUN WHITE: Basically, it's my unnatural way of riding.
[36:28] I do four full rotations with two flips in the middle.
[36:31] JASON DAVENPORT: I tried stopping using the camcorder,
[36:34] and I couldn't do it.
[36:35] So let's slow this down.
[36:37] And to do this, we need to analyze this frame by frame
[36:40] and see what Google Cloud sees.
[36:42] And we're going to do this essentially
[36:44] with every single stop in this.
[36:46] So first, let's start with your pose.
[36:49] So this is what's super cool.
[36:51] We built a model in collaboration
[36:53] with Google DeepMind that can track you spatially,
[36:56] and it creates a three-dimensional,
[36:57] essentially pose of you from a flat, two-dimensional video.
[37:01] SHAUN WHITE: So awesome.
[37:02] JASON DAVENPORT: I mean, I love it.
[37:03] I can't even see you here.
[37:05] How does it do it?
[37:06] SHAUN WHITE: This was my tight pant, leather jacket phase.
[37:09] So you got to really punch in on it.
[37:11] JASON DAVENPORT: I know.
[37:13] I got some out back, too.
[37:14] SHAUN WHITE: It was a good look.
[37:15] JASON DAVENPORT: I know.
[37:16] It's still a good look.
[37:17] All right.
[37:17] Let's talk about the next thing.
[37:19] These are stats powered by Gemini.
[37:21] Here, we're tracking your flight dynamics, rotational velocity,
[37:25] and even your tuck compression.
[37:27] SHAUN WHITE: Yeah, this is amazing.
[37:28] We didn't have this before.
[37:29] I can see how much time I've spent in the air.
[37:31] And now take old footage that I've done
[37:34] and compare it with new footage of how much time was I
[37:36] in the air when I landed the trick,
[37:38] and when I didn't land the trick.
[37:39] And I can compare the two.
[37:40] And that data is really going to help me progress.
[37:42] And obviously, the next generation
[37:44] can use these tools too.
[37:45] JASON DAVENPORT: It's true.
[37:46] And what I love is I love seeing how
[37:47] quickly your rotational velocity gets up here.
[37:50] It's all about how you exit the pipe.
[37:52] SHAUN WHITE: Torque-- exactly, and coming out of it.
[37:54] JASON DAVENPORT: All right.
[37:54] So let's look at one more cool thing that we've done here,
[37:56] which is the ribbon overlay.
[37:58] SHAUN WHITE: Oh, I love this.
[37:58] JASON DAVENPORT: So here's what I love about this.
[38:01] We're actually showing your cork here
[38:02] with the whole visualization.
[38:04] So what happens here?
[38:05] We're moving from blue to green on the thing.
[38:08] What is that?
[38:08] SHAUN WHITE: When I was working with the engineers,
[38:10] I was trying to describe this trick.
[38:12] And there's a turning point where normally I
[38:14] do one front flip and then come in for the second,
[38:17] but then you have to pause and then go backflip.
[38:20] If you miss that turning point in the trick, well,
[38:23] it's not good.
[38:25] It's not good.
[38:26] But this really pinpoints it exactly.
[38:28] JASON DAVENPORT: It is so cool to watch this.
[38:29] And what I love is that this is making sports
[38:31] more accessible for fans.
[38:33] We can see all of the craziness that you're
[38:35] putting into this sport.
[38:36] SHAUN WHITE: Exactly.
[38:37] JASON DAVENPORT: All right.
[38:38] So let's bring this back and talk a little bit more tech.
[38:41] Google Cloud is helping everyone innovate here
[38:44] by creating new use-case specific models, even
[38:46] for things like spatial analysis,
[38:49] training these large amounts of data and even models
[38:53] with Google Cloud TPUs, and helping everyone
[38:56] to build a secure data and agent foundation with Gemini
[38:59] Enterprise Agent Platform.
[39:02] Compute, models, and platform-- this
[39:05] is the power of Google Cloud AI.
[39:07] SHAUN WHITE: And look, from my personal experience,
[39:09] I can tell you, learning a trick on the mountain is one thing,
[39:12] but actually understanding the physics of a trick
[39:15] is a whole other thing.
[39:16] And this is really going to help not only
[39:18] the next generation of athletes learn new skills
[39:21] but also help the fans at home understand the sport better.
[39:24] And not just for snowboarding, but I think sports globally.
[39:28] Look, I'm so stoked to see what's ahead.
[39:30] Thank you, Jason.
[39:31] And thank you, Google Cloud Next.
[39:34] [APPLAUSE, UPBEAT MUSIC]
[39:39] SPEAKER 1: Please welcome SVP and chief technologist,
[39:42] AI and infrastructure, Amin Vahdat.
[39:47] [UPBEAT MUSIC]
[39:59] AMIN VAHDAT: Thank you, Shaun.
[40:00] We're taking the exact same technology
[40:03] that empowers athletes and applying it to the enterprise.
[40:06] Because while athletes use data to understand the game,
[40:08] the enterprise needs it to change the game.
[40:11] That kind of scale is the ultimate stress test
[40:14] for a unified system.
[40:15] When you process massive video feeds, reaching over
[40:18] decades of historical data, and defend
[40:20] every byte in near real time, you
[40:22] have a foundation built for anything.
[40:26] But to solve this at a global scale,
[40:28] we first have to solve a physics problem.
[40:31] It is a constant struggle against the limits
[40:33] of software architecture, hardware architecture, power,
[40:37] cooling, and even the speed of light.
[40:40] This is the foundation of our AI Hypercomputer.
[40:43] We integrate clean energy, massive physical scale,
[40:47] and our purpose-built infrastructure
[40:48] into a single unified engine of efficiency and innovation.
[40:54] Because in the agentic era, compute
[40:55] is no longer defined by chip.
[40:57] Compute is the entire data center.
[41:02] We give you the ultimate flexibility
[41:04] to pick the right architecture without compromise.
[41:07] You can see this in how different
[41:08] industry leaders are innovating on Google Cloud.
[41:11] Axia Energia, Brazil's largest power company,
[41:14] helps prevent power outages for millions of customers
[41:17] by running advanced weather modeling on TPU clusters.
[41:21] By forecasting severe weather conditions up
[41:23] to 10 days in advance, the company
[41:25] can proactively plan and execute preventative measures
[41:28] before storms hit, significantly reducing service interruptions
[41:33] year over year.
[41:35] Thinking Machine Labs shows our NVIDIA-based infrastructure
[41:38] to power Tinker, an open platform
[41:41] for reinforcement learning and fine-tuning
[41:43] of frontier models for specialized use cases.
[41:47] Woven by Toyota achieved 42% faster training
[41:50] for models that predict complex traffic events.
[41:54] The US Department of Energy is powering AI coscientists
[41:58] across all 17 national labs to accelerate the pace
[42:02] of scientific discovery.
[42:05] And Boston Dynamics is training and safely
[42:08] deploying flexible vision-language models
[42:10] to scale robotics across diverse industrial applications.
[42:16] As we enter the agentic era, we see
[42:18] that the demands of training and serving
[42:20] have completely diverged.
[42:22] To meet the performance needs of these explosive workloads,
[42:26] I am proud to announce our eighth-generation TPUs.
[42:31] [UPBEAT MUSIC]
[42:43] [APPLAUSE]
[42:51] These are things of beauty.
[42:53] For the very first time, we are launching
[42:55] two specialized platforms, each built
[42:57] from the ground up for the distinct demands of training
[43:00] and serving.
[43:02] TPU 8t is a powerhouse optimized for training.
[43:06] We have redefined performance capability
[43:08] by moving block scale multiplication directly
[43:11] inside the MXUs.
[43:13] This native MXU quantization eliminates VPU overhead,
[43:17] delivering nearly three times the compute performance per pod
[43:19] over previous generations.
[43:22] This allows us to push the absolute limits of model flops
[43:25] utilization at a massive scale and reduces the training time
[43:28] of frontier models.
[43:31] It leverages our breakthrough inter-chip interconnect
[43:33] technology, which now delivers twice the bandwidth compared
[43:36] to Ironwood, scaling up to 9,600 TPUs connected via our 3D torus
[43:42] topology.
[43:45] This is a 2.8x improvement over Ironwood to deliver 1.21
[43:49] exaflops of FP4 compute per pod.
[43:55] 8t provides two petabytes of shared bandwidth memory
[43:58] in a single superpod and utilizes the new TPUDirect
[44:01] storage to enable high-speed data transfers from managed
[44:05] storage.
[44:07] To put that scale in perspective,
[44:08] two petabytes is enough to hold the entire digital connection
[44:12] of the Library of Congress 100 times over.
[44:18] TPU 8i is optimized for the thinking part of the equation--
[44:21] inference and reinforcement learning.
[44:23] At the chip level, we have integrated a specialized
[44:26] collectives acceleration engine, which reduces latency
[44:29] by an additional 5x.
[44:31] By hosting the memory cache entirely on silicon,
[44:34] we've finally broken the memory wall that slows down
[44:36] long-context decoding.
[44:38] We then scaled this on-chip performance
[44:40] through our new board flight topology,
[44:43] which deploys 1152 TPUs into a single pod
[44:47] to run millions of concurrent agents with near-zero latency.
[44:52] This delivers 11.6 FP8 exaflops for a substantial 9.8x increase
[44:58] in performance over the 256-chip Ironwood pod.
[45:05] We are extending our infrastructure leadership
[45:08] to general purpose workloads as well with Google Cloud Axion.
[45:12] Our Google Axion N48 compute instances,
[45:15] powered by a custom-designed ARM CPU,
[45:17] deliver up to twice better price performance
[45:20] and 80% better performance per watt
[45:23] than comparable x86 instances.
[45:25] This provides a sustained, continuous operation
[45:28] designed to eliminate cold starts in logic apps,
[45:31] ensuring your agents are always on and ready to respond.
[45:36] We are the preferred cloud for NVIDIA GPUs
[45:39] by some of the largest scale and most innovative customers
[45:42] in the world.
[45:43] Today, I am pleased to announce that Google Cloud will
[45:46] be among the first to offer the NVIDIA Vera Rubin NVL72.
[45:51] [APPLAUSE]
[45:57] Vera Rubin NVL72 on Google Cloud achieves 10 times
[46:01] performance efficiency, optimized for high interactivity
[46:04] and long-context workloads.
[46:06] Of course, silicon is only half the battle.
[46:09] To feed these chips, we've redesigned our entire storage
[46:12] and networking pipeline.
[46:14] I am pleased to announce that Managed Lustre now supports
[46:16] throughput up to an industry-leading 10
[46:19] terabytes per second.
[46:21] We tied it all together with our new Virgo Network.
[46:24] Virgo doubles connectivity to scale training
[46:27] beyond the superpod.
[46:28] It links 134,000 chips with up to 47pb per second
[46:33] of non-blocking bandwidth to deliver over 1.7 million
[46:37] exaflops of compute.
[46:40] We can now turn months of training
[46:41] into weeks with the power of a million-plus TPU
[46:44] chips in a single cluster, all orchestrated
[46:48] by Pathways and JAX.
[46:52] [APPLAUSE]
[46:58] With up to four times the bandwidth
[47:00] per accelerator over the previous generation,
[47:02] this combination delivers near-linear scaling,
[47:04] ensuring you get the full power of every chip you pay for.
[47:08] We are also making Virgo available on NVIDIA, Vera Rubin,
[47:11] and NVL72, supporting up to 960,000 GPUs.
[47:18] Leading financial institutions are
[47:19] using this exact combination of silicon networking
[47:22] to gain a massive edge.
[47:24] Let's hear from Citadel Securities on how
[47:26] we are innovating together.
[47:29] JOSH WOODS: We trade over half a trillion dollars per day.
[47:32] But it's not just about volumes.
[47:34] It's also about speed.
[47:35] In financial markets, time is measured in nanoseconds.
[47:40] We connect markets all around the world,
[47:42] whether pension funds or somebody doing their first trade
[47:45] as part of their retirement, meet their investment
[47:48] objectives.
[47:49] We've partnered with Google Cloud
[47:50] to build what we think is the preeminent scalable, cloud-based
[47:54] research environment.
[47:56] For instance, we've been working in depth on TPU
[47:59] to ensure that we get the efficiency we
[48:02] need to run our business.
[48:06] Today, we're able to run workloads two to four times
[48:09] as fast with a 30% lower cost.
[48:12] HARIS NAIR: With TPU Ironwood, we
[48:14] can now run thousands of parallel chips
[48:16] for a single workload.
[48:17] And what that means for our business is workloads
[48:20] that used to take weeks or days, we
[48:23] can now execute those in hours or even minutes.
[48:27] Imagine you're a quantitative researcher.
[48:29] You have 100 novel ideas that you want to execute.
[48:32] Can we empower our researchers to be
[48:34] able to test all of their ideas and really
[48:36] be limited by not the scale of the platform
[48:39] or the economics of the problem, but by their own creativity?
[48:44] JOSH WOODS: As we like to say, steel sharpens steel.
[48:46] Google Cloud and Citadel Securities' collaboration
[48:49] has been exactly that.
[48:54] [APPLAUSE]
[49:00] AMIN VAHDAT: Citadel Securities is just one example
[49:02] of many innovators who are gaining a competitive edge
[49:04] on our foundational AI Hypercomputer.
[49:07] At this scale, you cannot have humans manually troubleshooting
[49:10] configurations.
[49:12] You need a cloud that drives itself.
[49:15] We have used the Model Context Protocol to turn every Google
[49:18] Cloud service into a tool that agents can orchestrate directly.
[49:22] By integrating Gemini reasoning into our own telemetry,
[49:26] the system now performs autonomous root cause analysis,
[49:29] identifying and fixing misconfigurations
[49:32] before you even realize there's a problem.
[49:35] This is the AI Hypercomputer-- purpose-built infrastructure
[49:39] to accelerate the entire AI lifecycle.
[49:42] To show you how we feed this engine with your data,
[49:44] please welcome Karthik Narain.
[49:47] [UPBEAT MUSIC]
[50:01] KARTHIK NARAIN: Thank you, Amin.
[50:03] Amin just showed you how we built the world's most--powerful
[50:06] reasoning engine.
[50:08] But here's the reality.
[50:09] Reasoning without context is just a guess.
[50:13] And when you expect your AI to make decisions and your agents
[50:16] to take actions, you cannot afford to guess.
[50:20] Trusted context turns an intelligent guess
[50:23] to a decisive action.
[50:26] Today, we are completely rethinking the data platform.
[50:29] We call it the Agenetic Data Cloud.
[50:34] To make this real, I'm thrilled to share
[50:37] four foundational innovations.
[50:40] It all starts with the foundation of truth.
[50:43] Right now, your data is trapped in PDFs, video calls,
[50:47] multiple data stores, and SaaS applications.
[50:51] How do we help AI make sense of it all?
[50:54] Today, we are introducing the Knowledge Catalog,
[50:57] a universal context engine for your enterprise.
[51:01] Starting with your structured world,
[51:03] the Knowledge Catalog natively integrates
[51:06] with BigQuery, mapping tables and metadata
[51:09] into unified business logic.
[51:12] And we are going even further, extending the same capability
[51:15] to the unstructured world as well through Smart Storage.
[51:20] In the past, a file landing in your storage
[51:22] sat passive, waiting for a pipeline.
[51:25] Now, the second that image or PDF hits Google Cloud Storage,
[51:30] they are instantly tagged, enriched, and made agent-ready--
[51:34] zero manual data engineering.
[51:37] Powered natively by Gemini, the Knowledge Catalog
[51:40] goes even deeper, reading files, autonomously extracting
[51:44] entities, mapping relationships, and learning
[51:48] about your unique business semantics.
[51:51] When an agent hears net revenue or risk,
[51:54] it understands the exact meaning.
[51:57] Take Virgin Media O2.
[51:59] They had over 20,000 data assets,
[52:01] many of which were untapped.
[52:04] Knowledge Catalog is helping them
[52:06] activate this data to empower their global product teams.
[52:11] Now, combined with direct zero-copy access
[52:14] to applications, operating systems, and AI platforms
[52:17] like Palantir, Salesforce, SAP, ServiceNow, and Workday,
[52:22] you now have instant context to your entire business.
[52:28] [APPLAUSE]
[52:30] That's right.
[52:33] This trusted context enables agents to act with certainty.
[52:39] Now imagine this power in the hands
[52:41] of every employee, every day.
[52:44] We've integrated the Knowledge Catalog with Gemini Enterprise's
[52:48] Deep Research Agent.
[52:50] You can now enable multi-step reasoning
[52:53] across internal and web data for precise cited business answers.
[52:58] What took weeks of manual effort now happens seamlessly.
[53:05] This is the Knowledge Catalog.
[53:08] Second, we are unveiling a Gemini-powered data science
[53:12] authoring experience that fundamentally transforms
[53:16] how data practitioners work with our new Data Agent Kit.
[53:22] We didn't just want to build another platform
[53:24] you have to learn.
[53:25] Instead, the Data Agent Kit integrates comprehensively
[53:31] with libraries of AI skills and plugins directly
[53:34] into your workflows that you already use.
[53:37] Your IDE, your notebook, your terminal,
[53:40] whether you are in VS Code, Cloud Code, or Gemini CLI,
[53:44] we instantly turn your everyday workspace
[53:47] into a native data environment.
[53:49] Here's how it works.
[53:51] You just state your intent, predict customer churn,
[53:56] and your environment simply takes over.
[53:59] It autonomously builds the pipelines
[54:01] and deploys the models right on Google's Agentic Data Cloud.
[54:06] It handles all the complex orchestration for you,
[54:09] creating a direct path from your business objective
[54:12] straight to the business outcome.
[54:15] For researchers at Bayer Crop Science,
[54:17] this eliminates manual data management and analytics,
[54:21] freeing them to focus on pioneering agricultural
[54:24] innovations for a sustainable and productive future.
[54:30] Third, the demand on data is extreme in this agentic era.
[54:35] To address the scale challenge, we're
[54:37] unleashing the Lightning Engine for Apache Spark.
[54:41] It not only delivers industry-leading performance
[54:44] for Spark.
[54:45] Lightning Engine delivers up to 2x the price performance over
[54:50] the previous market leader.
[54:52] [APPLAUSE]
[54:57] We fundamentally rebuilt this engine for the agentic era,
[55:01] reasoning with trusted context enables autonomous agents,
[55:05] and with the Lightning Engine, that autonomy
[55:08] operates at massive scale.
[55:12] Industry-leading organizations like Flipkart,
[55:14] Lowe's, and Meesho are accelerating their Apache Spark
[55:18] workloads with Lightning Engine.
[55:23] Finally, let's address the biggest
[55:25] and the longest-running debate of all.
[55:28] Where should your data reside?
[55:30] The reality is it lives everywhere--
[55:33] at Google, at AWS, Azure, and across your SaaS applications.
[55:38] Your old lakehouse expected the analytical engine and the data
[55:42] storage to reside in the same cloud.
[55:45] This approach is broken.
[55:48] Today, we are introducing the Cross-Cloud Lakehouse,
[55:53] where your analytical engine reasons over data in any cloud.
[55:59] [APPLAUSE]
[56:04] Built on the open Apache Iceberg standard,
[56:07] it is completely borderless.
[56:09] Instead of forcing you to accept complex networking hurdles
[56:13] or massive egress fees, we deliver low-latency direct
[56:16] connectivity to AWS and Azure as if the data sat natively
[56:21] in Google Cloud--
[56:23] no more moving data, no more vendor lock in-- just freedom.
[56:29] [APPLAUSE]
[56:33] No matter where your data lives, from traditional systems
[56:36] to SaaS applications, we provide the trusted context
[56:40] and connectivity your business needs to act upon.
[56:44] This is why customers are choosing our Agentic Data
[56:48] Cloud to drive their agentic transformation.
[56:53] One example of this transformation is Vodafone.
[56:57] Vodafone unified their data on BigQuery
[57:00] to build a more resilient global network for their customers.
[57:04] And with Gemini Enterprise, they are launching hundreds of agents
[57:08] to proactively resolve outages and optimize infrastructure,
[57:12] scaling network reliability and saving millions every year.
[57:18] Macquarie Bank's goal was to securely scale
[57:21] AI across their entire operation to deliver
[57:24] more personalized customer experiences.
[57:27] They deployed Gemini Enterprise to all staff
[57:30] and supercharged productivity.
[57:32] And for their two million customers,
[57:34] Macquarie's 24 by 7 AI assistant queue autonomously
[57:39] answers banking questions.
[57:42] All of this was made possible by unifying their data
[57:45] on BigQuery and Spanner.
[57:48] With this foundation, Macquarie has already cut client losses
[57:52] from scams by half.
[57:55] That's secure, frictionless banking at scale.
[58:00] American Express is redefining trust
[58:03] by centralizing its core data on Google Cloud
[58:06] to enable faster fraud and risk analysis at global scale.
[58:11] Costco leverages BigQuery to accelerate member insights,
[58:15] empowering associates to optimize experience and maximize
[58:19] value for millions of members.
[58:22] This is the Agentic Data Cloud.
[58:25] You and your AI agents now have a platform
[58:27] that operates with universal context,
[58:30] is defined by your defined intent,
[58:33] and scales autonomously across your borderless data
[58:37] and the applications you already use today.
[58:40] [APPLAUSE]
[58:44] To show you how to bring trusted context to your business,
[58:48] please welcome Yasmeen Ahmad.
[58:51] [UPBEAT MUSIC]
[59:02] YASMEEN AHMAD: Karthik gave us the recipe,
[59:04] so now let's see how it all plates up.
[59:07] Most companies take five weeks to turn a trend into a decision.
[59:11] We're going to do it in five minutes.
[59:14] To get there, we're breaking three barriers--
[59:17] dark data with our Knowledge Catalog, data
[59:20] silos with our Cross-Cloud Lakehouse, and manual code
[59:23] with Google's new agentic tools and skills.
[59:27] So let's get cooking.
[59:29] Our new agentic workflow triggers
[59:31] have detected a tasty trend--
[59:33] Midnight Swirl froyo.
[59:36] Looks delicious.
[59:37] But with any new flavor, I need to know is it safe?
[59:41] Are there any hidden allergens?
[59:43] Is there a market?
[59:44] Where are my hungry customers?
[59:46] And is it worth it?
[59:47] What's the real ROI?
[59:49] But first, safety.
[59:52] We have thousands of PDF recipes.
[59:55] I'm going to search for soy.
[59:57] It's a top food allergen, and safety is non-negotiable.
[60:01] Zero results-- looks good so far.
[60:05] But this recipe does contain an ingredient, base 204.
[60:10] Let's check the supplier manual for base 204.
[60:14] And here it is.
[60:16] Base 204 actually contains soy.
[60:19] A simple GenAI search would miss this connection
[60:22] because the information is trapped
[60:25] across two separate PDFs.
[60:27] So how do we digest all of this data
[60:30] to find those hidden connections?
[60:32] Well, I need an agent that has the skill
[60:35] to work with my PDF data combined with our Knowledge
[60:38] Catalog.
[60:40] Watch.
[60:41] Our agent helps us find hidden allergens
[60:43] in a recipe for Midnight Swirl.
[60:46] And here it is.
[60:48] Midnight Swirl contains soy, and it's giving me a data citation,
[60:53] a schema called product specs.
[60:55] Now, this schema was generated by our Knowledge Catalog,
[60:59] working with Gemini to reason over our supplier and recipe
[61:03] PDFs and extract entities like recipe, ingredient, and allergen
[61:09] and mapping those previously invisible connections
[61:12] between them.
[61:13] This is trusted context built over dark data.
[61:18] Next, market size-- to launch this globally,
[61:22] I need a precision list of customers
[61:24] with zero soy allergies.
[61:27] However, I have two data sets that have never met.
[61:30] Our allergen schema is here in Google,
[61:33] while my loyalty list with millions
[61:36] of customers and dietary preferences
[61:38] is in AWS S3 Iceberg.
[61:42] Now, usually you'd call your data engineer
[61:44] and wait three days for a migration.
[61:47] Not today.
[61:48] No more slow churn.
[61:51] With our cross-cloud Lakehouse, the data
[61:53] stays exactly where it is while we dynamically build the list.
[61:58] Before writing a single line of code,
[62:00] Gemini builds a plan using our data engineering
[62:03] tools while respecting my built-in security
[62:06] and permissions.
[62:07] As the data scientist, you're the chef.
[62:11] You can review.
[62:13] Let's accept and see what our agents serve up.
[62:17] They execute on our Lightning Engine,
[62:20] which is two times more price-performant than the market
[62:23] proprietary alternative, filtering
[62:26] our soy-sensitive customers in mere seconds.
[62:32] AWS and BigQuery sources connected--
[62:35] zero complexity.
[62:36] It's as smooth as a batch of Midnight Swirl.
[62:40] Finally, ROI-- is this worth a global launch?
[62:44] An annual forecast would help.
[62:48] Again, our agents don't guess.
[62:50] Gemini builds a multi-step execution plan.
[62:54] Can you make changes?
[62:55] Yes, Chef.
[62:57] So let's take a look and change the number of simulations from
[63:02] 1,000 to 2,000.
[63:06] And we will accept.
[63:10] Acting as the orchestrator, Gemini delegates
[63:13] to our specialized data science tools,
[63:15] choosing the right models, and again,
[63:17] executing on our Lightning Engine for Spark.
[63:21] It's built an entire notebook for me.
[63:24] Now, training usually takes a bit of time,
[63:26] so here's one I ran earlier.
[63:29] $15 million.
[63:32] Even after protecting our soy-sensitive market,
[63:35] the demand is massive.
[63:37] Can we bring back the timer?
[63:39] Look what we accomplished.
[63:41] In less than five minutes, we turned a viral trend
[63:44] into a $15 million decision.
[63:47] First, Knowledge Catalog found the hidden soy connection.
[63:51] Second, Cross-Cloud Lakehouse, powered by the Lightning Engine,
[63:55] connected to AWS with zero complexity.
[63:59] And third, our agentic skills and tools built the forecast
[64:03] with zero manual code.
[64:05] This is the Agentic Data Cloud.
[64:08] [TIMER BEEPING]
[64:10] [CHEERING, APPLAUSE]
[64:17] We did it.
[64:18] And now, here to share how we're securing
[64:21] your agentic enterprise, please welcome Francis deSouza
[64:26] [UPBEAT MUSIC]
[64:40] FRANCIS DE SOUZA: In an agentic enterprise,
[64:42] giving agents the power to act means
[64:45] that security must become an autonomous force, moving faster
[64:49] than the threats themselves.
[64:51] Human analysts simply can't keep up with AI-driven attacks.
[64:56] The mean time to exploit the vulnerability
[64:59] has dropped to minus seven days, meaning
[65:01] that today exploits routinely occur before a patch is
[65:06] released, and the handoff time between initial access
[65:10] and handover to a secondary threat group
[65:13] has dropped from eight hours to 22 seconds.
[65:17] Your security must operate at machine speed.
[65:21] Now, to do that, we're moving the heavy lifting
[65:24] to a Gemini-native agentic Security Operations
[65:28] Center, or SOC.
[65:30] And the results are already here.
[65:32] Our triage agents are turning 30-minute investigations
[65:36] into 60-second resolutions.
[65:40] And our new threat hunting and detection agents
[65:43] proactively sweep your environments
[65:44] for risks at a scale and speed that no human team could match.
[65:51] By leveraging Google's unparalleled telemetry,
[65:54] including Mandiant, VirusTotal, and Chrome,
[65:57] we are delivering a defense system
[65:59] based on global intelligence.
[66:02] And now, with our integrated dark web intelligence,
[66:06] we can identify external threats with 98% accuracy.
[66:11] Customers are already seeing big benefits
[66:13] from Google Cloud's security offerings.
[66:16] CME Group depends on a unified stack of Mandiant Security
[66:21] Command Center and Google's SecOps
[66:23] to protect the world's largest derivatives
[66:26] system against novel threats.
[66:30] In an ultra high-stakes environment,
[66:33] Google's security offering is matched by speed.
[66:37] CME Group achieves nanosecond precision
[66:40] with Google's ultra low-latency solution,
[66:43] supporting billions of transactions
[66:46] daily and lowering the barrier of entry for new traders.
[66:50] Now, the biggest threats today aren't just hackers.
[66:53] It's also shadow AI, unauthorized models and agents
[66:58] that are operating in your enterprise
[67:00] but outside your control.
[67:02] And to meet this challenge, we have
[67:04] integrated the industry's deepest security context
[67:08] directly into our AI fabric.
[67:11] That is why I am thrilled to officially welcome
[67:14] the Wiz team to Google Cloud.
[67:17] [APPLAUSE]
[67:24] Together, we are building a new security posture
[67:27] for the agentic enterprise.
[67:29] While the New agent platform in Gemini Enterprise
[67:32] secures and governs your agents, with Wiz,
[67:35] we're now extending that protection
[67:37] to every asset on your premises and across all major clouds.
[67:43] To explain how we're solving this,
[67:45] I'm excited to introduce to the stage Wiz co-founder Yinon
[67:49] Costica.
[67:51] [UPBEAT MUSIC]
[68:07] YINON COSTICA: Thank you, Francis.
[68:09] At Wiz, our mission from day one has
[68:12] been to help customers protect everything they build and run.
[68:17] Wiz began by unifying code and cloud and runtime context
[68:21] to move at developer speed.
[68:23] But AI has fundamentally changed the environment.
[68:28] With autonomous products, hyperscale code generation,
[68:31] and AI-weaponized threats, the stakes have never been higher.
[68:36] To protect this new frontier, security
[68:39] must now move at machine speed.
[68:42] And that is exactly why we built Wiz
[68:45] as the first AI Application Protection
[68:48] Platform, or in short, AI APP.
[68:51] It solves four key security challenges for the AI era--
[68:55] giving security unparalleled visibility into the AI stack,
[69:00] finding and proactively remediating critical risks
[69:05] before attackers can use them, enabling our builders
[69:09] to start secure by design in their AI
[69:12] enabled IDEs, and lastly, arming SecOps teams to stop threats
[69:17] to cloud and AI environments.
[69:19] In order to outpace attackers, Wiz
[69:22] delivers a set of expert AI agents.
[69:26] It's a really cool concept.
[69:28] Our red, blue, and green agents are
[69:32] named after the teams they help-- red, blue,
[69:35] and green teams.
[69:36] They form an AI layer to autonomously identify,
[69:40] investigate, and then fix critical risks at machine speed,
[69:44] leveraging context from our unique security graph.
[69:49] Security starts with visibility, always.
[69:52] But today.
[69:53] Your AI footprint is an orbit of interconnected tools.
[69:57] It spins models like Gemini, Claude, OpenAI.
[70:01] Your teams are using agent studios
[70:03] such as Gemini Enterprise Agent Platform, Lovable, Copilot
[70:09] Studio, Salesforce's Agentforce.
[70:12] And to secure all of these environments,
[70:14] we examine every layer--
[70:17] the clouds, the data, the models, the agents.
[70:21] And we continuously correlate these risks
[70:24] to find and fix the attack path that matter most.
[70:28] Now let's see it in action.
[70:31] [UPBEAT MUSIC]
[70:40] So this is Wiz.
[70:42] Wiz basically starts by automatically building
[70:45] a dynamic inventory from your code and cloud,
[70:48] completely agentlessly.
[70:50] This means that you can see every visibility
[70:54] into the environment, into everything
[70:56] that your teams are building using AI, without any friction.
[71:02] Wiz then builds the security graph.
[71:05] The security graph-- think of it as a living map that
[71:08] explains the architecture and the logic of any AI application
[71:12] it sees.
[71:13] Here we see an example of an agent that
[71:15] is actually running on cloud.
[71:18] It has tools to query a database and even execute code.
[71:22] And you can see that Wiz actually
[71:25] flags that this agent is internet-exposed.
[71:29] And also, it has access to sensitive customer data.
[71:33] Now, this is security moving at the speed of AI.
[71:38] You don't need any reviews with your development teams.
[71:41] It just works.
[71:43] And in the AI era, the best defense
[71:47] is to continuously use AI against ourselves
[71:50] in order to give the defenders the first-mover advantage
[71:55] over the attackers.
[71:56] And this is exactly what the Wiz Red Agent does.
[72:00] It validates every single exposure that Wiz identified.
[72:04] And you can think of it as a friendly hacker
[72:07] that continuously scans your outside like an elite red team.
[72:12] And look, this is a finding that the Red Agent actually
[72:16] found-- an authentication bypass vulnerability.
[72:20] This means that it enables an attacker
[72:23] to gain access to that agent but also to that sensitive database
[72:28] behind it to exfiltrate sensitive information.
[72:32] Now, it's not a potential risk anymore.
[72:35] This is a validated risk.
[72:37] And this gives our developers the prioritization
[72:41] and the confidence in fixing it immediately.
[72:44] Now, in the past, triage used to mean very, very long meetings
[72:49] and waiting and spreadsheets.
[72:51] And you don't need that anymore because we have the Wiz Green
[72:56] Agent.
[72:57] The Wiz Green Agent automates the entire triage process
[73:02] from identifying the owner, suggesting the fix,
[73:06] identifying the exact line of code that
[73:09] caused the risk in the first place--
[73:11] all automated-- no phone tag, no friction.
[73:14] And from here, you have a choice.
[73:15] You can send it to the developers as a PR,
[73:18] or maybe better, send it to a coding agent like CodeMender
[73:23] to execute the final fix at the source automatically.
[73:26] And this is how we can move from a validated risk
[73:30] to a verified fix, end-to-end visibility, control,
[73:34] and full confidence.
[73:36] So to summarize, whenever you have an AI team that
[73:40] ships a new product or even someone in finance
[73:44] that then codes a new agent, now, within minutes, security
[73:48] is able to, one, identify the agent and its architecture, two,
[73:53] automatically conduct a security review to find and validate
[73:57] the risks, and three, automatically suggest the fix,
[74:00] send it to the dev teams in their own native tools.
[74:03] Ta-da.
[74:04] This is how AI agents unlock secure innovation
[74:08] with AI at scale.
[74:11] Thank you, all.
[74:12] Back to you, Francis.
[74:14] [APPLAUSE, UPBEAT MUSIC]
[74:23] FRANCIS DE SOUZA: The unique combination
[74:24] of Google's agentic SOC and Wiz's deep cloud context
[74:29] gives customers the confidence to build and deploy generative
[74:32] AI everywhere safely.
[74:35] We're helping the Los Angeles Department of Water and Power
[74:38] secure critical infrastructure ahead of the LA 28 games,
[74:43] while Singapore's CSIT enables proactive defense
[74:47] against advanced digital threats.
[74:50] DBS strengthens security by embedding Google Cloud's
[74:53] protection directly into their architecture,
[74:56] enabling real-time threat detection and response,
[74:59] reinforcing customer trust.
[75:02] Morgan Stanley chose Wiz as a key component of its cloud
[75:06] security strategy and is expanding visibility and control
[75:10] across its cloud environments.
[75:12] Now, as part of this strategy, the firm
[75:14] is deploying Google Cloud Security capabilities to support
[75:18] its evolving cloud platforms.
[75:21] And trusted global icons from Nestlé
[75:24] to LVMH, from BMW to Shell, rely on Wiz
[75:28] and Google Cloud Security to ensure their AI-driven future
[75:32] is secure by design.
[75:34] We have closed the loop between the defender and the builder.
[75:38] Google and Wiz provide a unified agentic defense
[75:43] that is secure by design and autonomous by nature.
[75:47] Together, we are redefining cybersecurity for the AI era
[75:52] so security teams can protect their organizations at machine
[75:56] speed.
[75:58] To share more about how our agents are
[76:00] helping your customers, please welcome Carrie Tharp.
[76:04] [UPBEAT MUSIC]
[76:20] CARRIE THARP: Thank you, Francis.
[76:22] A secure foundation gives you the confidence to innovate.
[76:26] At Google, we are using that foundation
[76:28] to solve two of your biggest challenges--
[76:31] serving your customers and helping your employees
[76:34] work smarter.
[76:35] We do this by deploying a coordinated agentic task
[76:39] force, specialized agents that don't just
[76:43] assist your team-- they operate alongside them.
[76:47] In the agentic era, an agent is no longer just a tool.
[76:52] It's a strategic extension of your business, built
[76:55] to expand your reach, deepen engagement, and personalized
[77:00] service at scale.
[77:02] Earlier this year, we launched Gemini Enterprise
[77:05] for Customer Experience, a suite designed
[77:09] to enhance the entire customer journey
[77:11] from the first moment of discovery
[77:14] to ongoing service interactions.
[77:17] Central to this is agentic commerce.
[77:20] We have introduced pre-built shopping and food
[77:24] ordering agents that handle everything
[77:27] from discovery to checkout entirely
[77:30] through natural language.
[77:31] By pairing these agents with our customer engagement agents,
[77:36] we've created a seamless journey from search
[77:39] to service, turning every interaction into revenue
[77:43] and retention opportunities.
[77:45] For example, with the food ordering agent,
[77:48] Papa John's is building a hyper-personalized system
[77:53] that remembers customer preferences to get
[77:56] the food to your door faster.
[77:58] During live interactions, Agent Assist
[78:01] coaches employees to deliver fast and more accurate
[78:05] answers to customer questions.
[78:08] For example, Best Buy is guiding shoppers
[78:11] through those complex tech specs,
[78:13] issue resolution, and appointment scheduling,
[78:17] all autonomously.
[78:19] And our new omnichannel gateway helps
[78:21] ensure your agents maintain contacts across every surface--
[78:26] web, mobile, voice-- while preserving universal consumer
[78:31] context.
[78:33] If a customer moves from a text chat to a phone call,
[78:37] the agent seamlessly remembers exactly where they left off,
[78:41] and this scales globally, conversing
[78:43] with human-like voice capabilities
[78:46] in multiple languages.
[78:48] Gemini Enterprise for Customer Experience
[78:51] is the technology powering the Home Depot's Magic Apron
[78:54] Assistant to reach customers across every channel.
[78:58] Let's hear from the Home Depot.
[79:00] JORDAN BROGGI: There's a customer journey
[79:02] that often starts with inspiration, goes into research,
[79:04] and then into shopping and buying and then into doing.
[79:07] And Magic Apron is really a digital agent
[79:09] to help through that whole journey,
[79:11] and it is really there to provide that orange apron
[79:15] experience for help and customer service, the same way
[79:18] that as you come into a store, you're used
[79:20] to that great customer service.
[79:23] So we've had a partnership with Google for quite some time,
[79:26] and the latest things that they're bringing to market
[79:28] are incredibly exciting to us.
[79:31] ANGIE BROWN: We're really excited about Magic Apron
[79:34] Shopping Assistant.
[79:36] Our relationship with Google has really made this possible
[79:38] in a variety of different ways.
[79:40] If you just focus for a second on Gemini Enterprise
[79:43] and you think about how it's the bedrock of what we are doing
[79:46] with Magic Apron, it gives us the power
[79:49] and the speed in which we need in order to power
[79:53] these great technologies.
[79:54] JORDAN BROGGI: Now, with Gemini Enterprise,
[79:56] we think that we can blend those channels because we'll
[79:59] have an agentic back end.
[80:01] Maybe most importantly, we see a higher conversion rate when they
[80:05] leverage the tool versus when they don't.
[80:08] When customers come into the store and they use Magic Apron,
[80:10] typically it's about wayfinding.
[80:12] It's about product knowledge.
[80:13] It's about compatibility.
[80:15] But they also use it at home on our website.
[80:17] Those experiences should all get better-- easier
[80:20] to be inspired, easier to find what I need, easier
[80:23] to order it and get it, easier to get support after the fact.
[80:26] ANGIE BROWN: The partnership really
[80:27] keeps us at the forefront of building
[80:29] great experiences for our customers and our associates.
[80:35] [APPLAUSE]
[80:41] CARRIE THARP: The same agentic approach
[80:43] is also reshaping digital-first industries.
[80:47] One example of this is Reliance.
[80:49] Reliance is transforming retail for millions in India
[80:53] with a shopping agent.
[80:55] Customers can simply type "plan a birthday party,"
[80:58] and the agent guides their journey across categories
[81:01] and assembles their cart, which drives more revenue per visit.
[81:06] Gemini-powered catalog enrichment
[81:09] supports this, analyzing millions of images
[81:12] in minutes instead of months.
[81:15] Gemini Enterprise for Customer Experience
[81:17] is hard at work across many industries and enterprises.
[81:22] Internally, teams at Google, including Google Store,
[81:26] Google Photos, Google One, and YouTube,
[81:30] are partnering with us to power our own scaled customer
[81:34] experiences.
[81:35] Let's take a look at how one of our first internal customers
[81:39] is doing this.
[81:40] Over to you, Patrick.
[81:42] [UPBEAT MUSIC]
[81:51] PATRICK MARLOW: All right.
[81:52] Thanks, Carrie.
[81:53] Hey, y'all.
[81:54] I'm Patrick Marlow, a senior product manager
[81:56] here at Google Cloud.
[81:57] YouTube TV customer support recently launched a voice agent
[82:00] to help users with NFL Sunday Ticket and YouTube TV plan
[82:04] subscriptions.
[82:05] It's currently live in production,
[82:07] serving 100% of their users.
[82:09] We're going to take a look at it in action today.
[82:11] I'm going to call the support number now.
[82:18] SPEAKER 4: Thanks for calling the YouTube TV and NFL Sunday
[82:21] Ticket Support Line.
[82:22] So tell me, how can I help you today?
[82:24] PATRICK MARLOW: Hey yeah, I've got
[82:25] a bunch of my friends coming over tomorrow night for the NFL
[82:27] draft.
[82:28] I don't have cable.
[82:29] And to be honest, I really only watch sports.
[82:31] Does YouTube TV have a plan that's just for that?
[82:37] SPEAKER 4: Yes, we do.
[82:38] The YouTube TV Sports Plan has exactly what
[82:41] you're looking for, plus over 30 other sports channels.
[82:45] It's $18 less per month than the base plan.
[82:48] Would you like me to text you a link to the number
[82:50] you're calling from that will take you directly--
[82:52] PATRICK MARLOW: Yeah, go ahead and send me a link.
[82:53] I'll take a look at it.
[83:00] SPEAKER 4: Great, I just sent the link to your mobile phone.
[83:02] Is there anything else I can help with today?
[83:05] PATRICK MARLOW: Yeah, I'm going to take a look at this now.
[83:07] Actually, my father-in-law is hanging out with me here today.
[83:10] He's been thinking about getting the sports plan for his account
[83:12] as well.
[83:13] I just wanted to see what he thinks about this.
[83:15] [SPEAKING SPANISH]
[83:19] Actually, could you just quickly summarize this plan for him
[83:21] in Spanish?
[83:22] I think he just wants to know that he can get both football e
[83:25] football.
[83:28] SPEAKER 4: [SPEAKING SPANISH]
[83:42] PATRICK MARLOW: Look, one other question.
[83:44] I was planning on setting up multiple TVs
[83:46] throughout the house, like one in the living room,
[83:48] one over on the patio.
[83:49] Would that be OK with this plan?
[83:53] SPEAKER 4: Definitely, with the Sports Plan,
[83:55] you can stream on up to three screens at the same time.
[83:58] Is there anything else?
[83:59] PATRICK MARLOW: Awesome.
[84:00] No, look, I'm actually in the middle
[84:02] of a stadium full of people right now,
[84:03] and I think there's millions watching me on the live stream.
[84:06] So I kind of got to run.
[84:07] All right.
[84:07] You've been helpful.
[84:08] Later.
[84:11] [APPLAUSE]
[84:16] All right.
[84:17] Now, that was pretty easy.
[84:19] The agent was able to handle complex product logic
[84:21] and seamlessly pivoted languages.
[84:23] But what happens if the team wants to make a change?
[84:26] They don't need a bunch of software engineers updating
[84:29] endless lines of code.
[84:30] They just need CX Agent Studio.
[84:33] Here, we have our Visual Builder, inside
[84:35] of CX Agent Studio, which is designed
[84:37] to give you complete transparency
[84:39] and control over your entire agent-building experience.
[84:42] This is the behind-the-scenes look
[84:44] at the actual YouTube TV's agent that they put in production.
[84:47] The orchestration is managed across
[84:49] multiple specialized agents, which can handle even
[84:52] the most complex requests.
[84:54] And with our built-in test interface,
[84:56] you can ensure that every answer is grounded, factual, and pulled
[85:01] directly from a knowledge base, like the Agents Price Finder
[85:04] tool here.
[85:05] And the best part?
[85:06] YouTube TV customer support built
[85:09] and deployed this entire experience in just six weeks.
[85:13] Now, let's say that the team wants to run a promotion.
[85:16] It's really simple for me to add a new subagent here.
[85:19] Click Add a New Subagent.
[85:21] And I will make this our new promotions agent.
[85:26] Say promotions.
[85:28] I'll hit Save.
[85:29] And then I'm going to quickly add
[85:31] a set of business logic via instructions in natural language
[85:36] to our agent.
[85:37] I'll hit Create.
[85:39] And just like that, our multi-agent system
[85:42] has instantly adapted to this new specialized agent.
[85:45] It's really that simple.
[85:47] Finally, with conversational insights,
[85:50] the YouTube TV team can instantly
[85:51] see how these calls are performing,
[85:53] all within the same platform.
[85:55] Here's one of my test calls from earlier this morning.
[85:58] Now, let's recap.
[86:00] We just turned a routine support call
[86:02] into a multilingual upgrade and a globally deployed promotion,
[86:06] all in under four minutes.
[86:08] This is the power of Gemini Enterprise
[86:10] for Customer Experience.
[86:12] It gives you enterprise-grade reasoning, deep tool
[86:15] integration, a powerful Visual Builder, and insights
[86:18] to iterate at the speed of your business.
[86:21] We can't wait to see what you will build next.
[86:25] Thank you.
[86:26] [UPBEAT MUSIC, APPLAUSE]
[86:30] SPEAKER 1: Please welcome VP of product,
[86:32] Google Workspace, Yulie Kwon Kim.
[86:36] [UPBEAT MUSIC]
[86:50] YULIE KWON KIM: Google Workspace is the world's most popular
[86:53] productivity tool.
[86:54] But even with the best tools at our fingertips,
[86:58] work can still feel incredibly fragmented.
[87:01] We've all been there.
[87:03] You're trying to answer one simple question,
[87:05] and 10 minutes later, you have 15 tabs open.
[87:09] You're jumping from a stale email
[87:11] to a slide deck that's being edited as you watch
[87:13] and digging through a spreadsheet
[87:15] just to find a quick answer.
[87:18] We all spend half our day finding information
[87:21] and the other half figuring out what to do with it.
[87:25] What if you could skip both?
[87:29] Today, we're introducing Workspace Intelligence.
[87:33] [APPLAUSE]
[87:37] With the advanced reasoning capabilities of Gemini
[87:40] and state-of-the-art embedding models,
[87:42] we're eliminating context fragmentation across
[87:45] the Workspace suite.
[87:48] Think of it as a unified intelligence
[87:50] layer that lives inside every Workspace app.
[87:53] It connects the dots and lets AI do the heavy lifting.
[87:57] Let's see it in action.
[88:00] [UPBEAT MUSIC]
[88:10] So remember that furniture rebrand you saw earlier?
[88:14] Let's say I'm the regional distributor,
[88:16] and here I am in Google Chat.
[88:19] This is where I collaborate with my colleagues and now my agents,
[88:22] all in one place.
[88:24] I'm here in the Regional Managers chat space.
[88:27] And look at this.
[88:28] The Regional Operations Agent, which
[88:31] we built in Gemini Enterprise, just
[88:34] alerted the team that our new display kits are arriving early.
[88:38] And as you can see, I have multiple chats blowing up.
[88:44] What do I need to do next?
[88:46] With Ask Gemini, Google Chat becomes my Command Center.
[88:53] Look here.
[88:54] Gemini tells me exactly what matters right now.
[88:58] See?
[89:01] It surfaced an urgent task.
[89:02] It linked the pitch deck that I need to localize,
[89:05] and it flagged my 4:00 PM deadline for the regional plan.
[89:09] The gathering is done.
[89:10] And I haven't opened a single extra tab.
[89:14] Now, to build this plan, I remember
[89:17] we had a great chart that showed regional sales last quarter--
[89:22] somewhere.
[89:23] Normally, this is where the hunt begins.
[89:26] I jump into a folder and stare at dozens of files.
[89:30] Instead, I'll ask Gemini.
[89:34] Find the merchandising playbook from last quarter,
[89:38] the one with the chart showing regional sales.
[89:41] This isn't just keyword matching.
[89:43] Workspace Intelligence understands
[89:45] the context of my meetings and the content inside my files.
[89:50] Here it is.
[89:51] It pointed me directly to the dock with the exact graph
[89:55] I need--
[89:56] a needle in a haystack, solved in seconds.
[90:00] Now I need to turn this scattered data
[90:03] into a regional plan.
[90:05] I have a standard format for briefing store managers,
[90:08] and with Ask Gemini, I basically say just do it for me.
[90:12] Watch this.
[90:15] Let's see, use the regional campaign skill to-- oops,
[90:25] create a deck with a plan for organic living.
[90:29] By tagging that specific skill, Workspace Intelligence
[90:33] goes to work.
[90:34] It cross-references my emails, my chats, my other docs.
[90:38] It pulls live HubSpot win-loss data.
[90:40] It uses our corporate branding, all to generate a new Google
[90:44] slide deck.
[90:46] It's hard at work pulling that deck together.
[90:49] And here it is.
[90:51] And in the citations up here, you
[90:53] can see all the sources Workspace Intelligence pulled in
[90:57] without me having to ask--
[90:59] the emails, the chats, the other files.
[91:03] Should we take a look?
[91:06] Yeah, look at this.
[91:09] It's beautiful.
[91:11] It matches my brand, and it's consistent with how
[91:14] I've structured all of my regional plan decks in the past.
[91:17] And it looks like it was designed by a creative pro, not
[91:22] a terminal.
[91:23] I'm ready to brief my team.
[91:26] Workspace Intelligence is the end of the context tax.
[91:30] It transforms how you work by turning fragmented information
[91:34] into a clear path forward.
[91:36] No complicated setup.
[91:38] It's secure.
[91:39] It's integrated.
[91:40] It just works.
[91:43] By moving from simple automation to full-scale AI orchestration,
[91:47] industry leaders are using Workspace to fundamentally
[91:50] redefine global productivity.
[91:53] For example, customers like Colgate-Palmolive rolled out
[91:57] Google Workspace to 34,000 employees.
[92:00] AI agents built on Gemini helped their teams drive innovation,
[92:04] turning data into new product concepts in minutes, not months.
[92:09] And they drive business growth by unlocking actionable insights
[92:12] from decades of sales history, empowering their teams
[92:16] to help brighten millions of smiles every morning.
[92:20] At Natura, custom agents are accelerating data-driven
[92:24] reporting by 10x, while at Korean Air,
[92:28] over 22,000 global employees use AI agents and tools
[92:32] to accelerate high-impact operational and care tasks.
[92:36] And at Compass Real Estate, Gemini
[92:39] is managing tasks for employees so they
[92:41] can focus on valuable client relationships.
[92:45] We know many companies want to make the shift to Workspace
[92:49] but worry about the complexity of migration.
[92:52] I'm thrilled to announce that migrating
[92:55] your entire organization, including complex and finance
[92:59] teams from Microsoft 365 to Google Workspace,
[93:03] is now up to five times faster, thanks to our new migration
[93:08] and interoperability enhancements.
[93:12] Today, you've seen what it looks like to fundamentally change
[93:16] how your employees operate.
[93:19] Go ahead, close those 15 tabs.
[93:22] Workspace Intelligence is now ready to do
[93:25] the heavy lifting for you.
[93:27] Thomas, over to you.
[93:29] [UPBEAT MUSIC, APPLAUSE]
[93:43] THOMAS KURIAN: Wasn't that amazing?
[93:45] [CHEERING, APPLAUSE]
[93:50] Thank you, Yulie.
[93:52] Before we close, let's take a look
[93:54] at one of the most respected brands in the world.
[93:57] Serving 3.7 billion people around the world,
[94:01] Unilever chose Google Cloud to build and deploy agents
[94:05] at scale.
[94:06] Let's hear their story.
[94:10] LEANDRO BARRETO: At Unilever, we serve 3.7 billion people
[94:13] every day with our products.
[94:15] We need to understand these individuals at a unique level,
[94:19] and that's what the partnership with Google Cloud
[94:22] allows us to do.
[94:23] It's about personalization at scale, desire at scale,
[94:28] and that's very unique.
[94:29] And it's at the core of our strategy.
[94:33] SAM KINI: We are connecting our business end-to-end
[94:35] for faster AI deployment across the enterprise.
[94:38] This means using AI to supercharge demand generation,
[94:42] get early access to advanced AI and large language models,
[94:45] and build even stronger partnerships with retailers,
[94:47] connecting data in smarter ways so we can grow together.
[94:50] Underpinning all of this is a strong digital backbone
[94:54] powered by Google Cloud.
[94:57] DAPHNE COATES: So my role is to operationalize
[94:59] this really exciting vision, co-creating that with Google
[95:02] to deliver our agentic foundations so
[95:04] that our agents can be architected for performance,
[95:08] scale, security, and observability right
[95:10] from the outset.
[95:12] The competitive buying multiagentic solution,
[95:14] co-created with our Horizon3 Labs,
[95:16] is leveraging core Google Cloud technologies such as Gemini
[95:20] Enterprise and ADK, your Agent Development Kit,
[95:22] and the underlying Gemini models.
[95:25] So this solution orchestrates a multitude of agents
[95:28] together through one single user interface.
[95:30] This helps my procurement colleagues
[95:32] make analysis and decisions in minutes instead of days.
[95:36] This solution has enabled us to make smarter sourcing decisions.
[95:40] SAM KINI: With Google Cloud, we are turning intelligence
[95:42] into a growth engine.
[95:47] [APPLAUSE]
[95:53] THOMAS KURIAN: There's one final difference
[95:55] in how we work with you.
[95:56] We believe the future of AI must be open.
[96:00] While others want to lock you into a wall garden that
[96:03] owns your models, your data, and your agents,
[96:06] we offer you an integrated stack,
[96:09] but the freedom to choose the world's best chips and models,
[96:13] the freedom to run AI wherever your data may live,
[96:17] the freedom to control your own destiny with deep governance
[96:20] features.
[96:21] We scale this mission through our partners,
[96:25] with whom we're building a broad and deep network
[96:28] of forward-deployed engineers, including Accenture, BCG,
[96:33] Deloitte, and McKinsey, who've announced
[96:35] major expansion of their Google Gemini AI practices.
[96:40] Along with AI-led service partners like Quantium, Distil
[96:45] and Tribe.AI, we're helping independent software vendors
[96:49] and SaaS companies transform their solutions with Gemini
[96:53] Enterprise Agent Platform.
[96:55] And we're bringing AI to small to medium-sized businesses
[97:00] by helping them adopt Gemini Enterprise and our AI
[97:04] advances in Workspace that work so seamlessly together now.
[97:09] We've covered a lot of ground today.
[97:11] We've introduced our agentic blueprint
[97:13] as a foundation for agents to transform your companies--
[97:18] first, the AI Hypercomputer, the purpose-built foundation
[97:22] optimized for the physics of the agentic era; second,
[97:26] the Agentic Data Cloud, the engine that provides agents with
[97:30] trusted business context; Agentic Defense,
[97:34] the autonomous protection that secures your entire AI
[97:38] lifecycle; fourth, the Gemini Enterprise Agent Platform,
[97:43] a new agentic platform with state-of-the-art models
[97:47] to build, deploy, and manage agents; and finally,
[97:50] the Agentic Task Force, the specialized agents that are
[97:53] already transforming customer experience and employee
[97:56] productivity and transforming workflows all served up to users
[98:02] to the Gemini Enterprise Application.
[98:05] To each of our customers and partners,
[98:08] thank you, each of you, for being on this amazing journey
[98:12] with us.
[98:14] Our teams have worked so hard this last year
[98:17] to prepare this amazing technology for you,
[98:21] and we're all so proud of the work they've done.
[98:24] This platform is ready.
[98:27] So what will each of you build?
[98:30] We have three fantastic days planned for you,
[98:33] and a great set of sessions and spotlights still to come.
[98:38] Thank you and have an amazing Cloud Next.
[98:42] [CHEERING, APPLAUSE]
[98:45] [MUSIC PLAYING]