Over 3,000 Product Advances

This highlights the rapid pace of innovation within Google Cloud and Workspace, indicating a significant investment and focus on integrating AI across their offerings. This suggests a broad strategy of embedding AI capabilities into existing and new services rather than just isolated AI products.

Gemini Models Adoption

The statistic of over 4 million developers using Gemini models underscores the widespread adoption and trust in Google's foundational AI models. This adoption rate suggests a strong developer ecosystem and the perceived utility of Gemini for various AI tasks.

Gemini 2.5 Pro

Described as their "next leap in performance," Gemini 2.5 Pro (with AI studio) promises significant improvements in accuracy, understanding, and speed improvement compared to previous generations, focusing on more capable and reliable general-purpose AI models.

Gemini 2.5 Flash

Positioned as a faster and more cost-effective version of Gemini, Flash caters to applications requiring low latency and high throughput, such as real-time interactions and processing large volumes of data quickly.

Veo2 (Advanced Video Generation Model)

The introduction of Veo  2 signifies Google's progress in generative AI beyond text and images, entering the complex domain of video creation. This technology has potential implications for content creation, marketing, and various other industries.

Investment in 2025

Google invests a massive 75B in infrastructure, underscoring its long-term commitment to building and scaling the infrastructure needed to support the growing demands of AI and cloud computing. It signals a proactive approach to ensuring sufficient capacity and cutting-edge hardware for their services.

Seventh-Generation TPU (Ironwood)

The development of custom Tensor Processing Units (TPUs) highlights Google's vertical integration strategy, optimising hardware specifically for AI workloads. Ironwood promises significant performance gains and efficiency improvements over previous TPU generations, directly benefiting AI training and inference tasks.

Making Google's Global Private Network Available

Opening up their internal network infrastructure to enterprises provides enhanced security, reliability, and performance for connecting to Google Cloud services.

Honeywell

Honeywell's use case involves applying Google Cloud (Vertex AI, BigQuery and Gemini) to industrial applications, such as optimising manufacturing processes, predictive maintenance of equipment, improving energy efficiency, or developing "Smart" building solutions.

Deutsche Bank

This case study focuses on how a major financial institution is leveraging Google Cloud for tasks like data analytics, risk management, fraud detection, and improving customer service through AI-powered tools.

Salesforce

The example showcases using Google Cloud and AI  agents (AgentForce) for applications such as optimising operations, leading human augmentation and customer success. 

Reddit

The use case highlights the beneficial blend of the AI agent and the search engine of the platform to improve the customer experience, making the search even faster and more relevant.

Comprehensive AI Platform

Emphasising Vertex AI as a central hub for building and managing AI agents underscores Google's strategy of providing a unified and streamlined development experience for AI practitioners. This platform aims to simplify the complexities of the AI lifecycle, from data preparation to model deployment and monitoring.

Agent Development Kit (ADK) for Multi-Agent Systems

The introduction of the ADK signifies a move towards enabling more sophisticated AI applications through the collaboration of multiple specialised agents. This framework aims to simplify the development of complex workflows where different AI agents can work together to achieve a common goal.

Google Agent Space for Deploying AI Agents

Agent Space aims to bridge the gap between AI development and real-world application by providing a managed environment for deploying and accessing AI agents within an enterprise context. This suggests a focus on making AI agents readily available to employees for various tasks.

Customer Agents

These agents focus on enhancing customer interactions through chatbots, virtual assistants, and personalised support, aiming to improve efficiency and customer satisfaction.

Creative Agents

This category likely showcases AI tools for content generation, such as text, images, and potentially video, empowering users in marketing, design, and media creation.

Data Agents

These agents focus on automating data-related tasks like data analysis, cleaning, preparation, and insights generation, helping businesses make better data-driven decisions.

Coding Agents

These agents assist software developers with tasks like code generation, debugging, and optimisation, potentially increasing developer productivity.

Security Agents

This category likely involves AI-powered tools for threat detection, vulnerability analysis, and automated security responses, aiming to enhance an organisation's security posture.

The introduction of GUS indicates a move towards a more integrated and holistic approach to security, combining various security tools and data sources into a unified platform. This aims to provide better visibility, faster threat detection, and more effective security management across an organisation's entire digital footprint.

NVIDIA

The partnership with NVIDIA focuses on leveraging their advanced GPUs to accelerate AI workloads within Google Cloud, ensuring access to cutting-edge hardware for demanding AI tasks. More details about the partnership also can be found here.

Salesforce

This partnership between Google Cloud capabilities and Salesforce's CRM platform enhances sales, marketing, and customer service workflows with AI-powered insights and automation.

Adobe

The collaboration with Adobe centres around integrating AI into creative workflows, potentially leveraging generative AI models for content creation and enhancing Adobe's suite of creative tools with Google's AI capabilities.