Google Cloud Next ’25: New AI chips and agent ecosystem challenge Microsoft and Amazon

Google Cloud is making big moves to cement its spot in the fiercely competitive world of artificial intelligence. At the annual Cloud Next conference in Las Vegas, they unveiled a suite of new technologies centered around "thinking models," agent ecosystems, and specialized infrastructure tailored for massive AI deployments.

The star of the show was the seventh-generation Tensor Processing Unit (TPU), dubbed Ironwood. Google boasts that it delivers over 42 exaflops of computing power per pod, which is a mind-blowing 24 times more powerful than the leading supercomputer, El Capitan.

“The opportunity with AI is as big as it gets,” Amin Vahdat, Google’s vice president and general manager of ML systems and cloud AI, exclaimed during a pre-event press conference. “Together with our customers, we’re powering a new golden age of innovation.”

Google's cloud business is riding a wave of momentum. In January, they reported a Q4 2024 cloud revenue of $12 billion, a 30% jump from the previous year. The company also noted an 80% increase in active users on AI Studio and the Gemini API over the past month.

How Google's New Ironwood TPUs Are Transforming AI Computing with Power Efficiency

Google is positioning itself as the only major cloud provider with a "fully AI-optimized platform," designed from the ground up for what they call "the age of inference." This shift focuses on using AI systems to tackle real-world issues rather than just training models.

Ironwood represents a significant shift in chip design philosophy. Unlike its predecessors, which balanced training and inference, Ironwood is specifically engineered to run complex AI models post-training.

“It’s no longer about the data put into the model, but what the model can do with data after it’s been trained,” Vahdat explained.

Each Ironwood pod packs over 9,000 chips and is twice as power-efficient as the previous generation. This addresses a major concern with generative AI: its massive energy consumption.

Google is also opening its vast global network infrastructure to enterprise customers through Cloud WAN (Wide Area Network). This service taps into Google’s 2-million-mile fiber network, the same one that powers consumer services like YouTube and Gmail.

According to Google, Cloud WAN can boost network performance by up to 40% and cut the total cost of ownership by the same amount compared to customer-managed networks. This move is unusual for a hyperscaler, essentially turning its internal infrastructure into a product.

Inside Gemini 2.5: How Google's 'Thinking Models' Improve Enterprise AI Applications

On the software front, Google is expanding its Gemini model family with Gemini 2.5 Flash, a cost-effective version of its flagship AI system that introduces "thinking capabilities."

Unlike traditional large language models that directly generate responses, these "thinking models" break down complex problems through multi-step reasoning and self-reflection. Gemini 2.5 Pro, launched just two weeks ago, targets high-complexity use cases like drug discovery and financial modeling. The newly announced Flash variant adjusts its reasoning depth based on prompt complexity to balance performance and cost.

Google is also beefing up its generative media capabilities with updates to Imagen (for image generation), Veo (video), Chirp (audio), and introducing Lyria, a text-to-music model. During the press conference, Nenshad Bardoliwalla, Director of Product Management for Vertex AI, showcased how these tools can collaborate to create a promotional concert video, complete with custom music and sophisticated editing like removing unwanted elements from video clips.

“Only Vertex AI brings together all of these models, along with third-party models onto a single platform,” Bardoliwalla said.

Beyond Single AI Systems: How Google's Multi-Agent Ecosystem Aims to Enhance Enterprise Workflows

Google's most forward-looking announcements focus on creating a "multi-agent ecosystem," where multiple AI systems can collaborate across different platforms and vendors.

They're introducing an Agent Development Kit (ADK) that allows developers to build multi-agent systems with less than 100 lines of code. Additionally, Google is proposing a new open protocol called Agent2Agent (A2A), enabling AI agents from different vendors to communicate.

“2025 will be a transition year where generative AI shifts from answering single questions to solving complex problems through agented systems,” Vahdat predicted.

Over 50 partners, including major enterprise software providers like Salesforce, ServiceNow, and SAP, have signed on to support this protocol, suggesting a potential industry shift toward interoperable AI systems.

For non-technical users, Google is enhancing its Agent Space platform with features like Agent Gallery (providing a single view of available agents) and Agent Designer (a no-code interface for creating custom agents). During a demonstration, Google showed how a banking account manager could use these tools to analyze client portfolios, forecast cash flow issues, and automatically draft client communications — all without writing any code.

From Document Summaries to Drive-Thru Orders: How Google's Specialized AI Agents Are Affecting Industries

Google is deeply integrating AI across its Workspace productivity suite, introducing features like "Help me Analyze" in Sheets, which automatically identifies insights from data without explicit formulas or pivot tables, and Audio Overviews in Docs, which create human-like audio versions of documents.

The company highlighted five categories of specialized agents seeing significant adoption: customer service, creative work, data analysis, coding, and security.

In customer service, Google pointed to Wendy’s AI drive-through system, which now handles 60,000 orders daily, and The Home Depot’s “Magic Apron” agent, which offers home improvement guidance. For creative teams, companies like WPP are using Google’s AI to conceptualize and produce marketing campaigns at scale.

Cloud AI Competition Intensifies: How Google's Comprehensive Approach Challenges Microsoft and Amazon

Google’s announcements come amid intensifying competition in the cloud AI space. Microsoft has deeply integrated OpenAI’s technology across its Azure platform, while Amazon has been building out its own Anthropic-powered offerings and specialized chips.

Thomas Kurian, CEO of Google Cloud, emphasized the company’s “commitment to delivering world-class infrastructure, models, platforms, and agents; offering an open, multi-cloud platform that provides flexibility and choice; and building for interoperability.”

This multi-pronged approach seems designed to set Google apart from competitors who may excel in specific areas but lack the full stack from chips to applications.

The Future of Enterprise AI: Why Google's 'Thinking Models' and Interoperability Matter for Business Technology

What makes Google’s announcements particularly significant is the comprehensive nature of its AI strategy, spanning custom silicon, global networking, model development, agent frameworks, and application integration.

The focus on inference optimization rather than just training capabilities reflects a maturing AI market. While training ever-larger models has dominated headlines, deploying these models efficiently at scale is becoming the more pressing challenge for enterprises.

Google’s emphasis on interoperability — allowing systems from different vendors to work together — may signal a shift away from the walled garden approaches that have characterized earlier phases of cloud computing. By proposing open protocols like Agent2Agent, Google is positioning itself as the connective tissue in a heterogeneous AI ecosystem rather than demanding all-or-nothing adoption.

These announcements present both opportunities and challenges for enterprise technical decision makers. The efficiency gains promised by specialized infrastructure like Ironwood TPUs and Cloud WAN could significantly reduce the costs of deploying AI at scale. However, navigating the rapidly evolving landscape of models, agents, and tools will require careful strategic planning.

As these more sophisticated AI systems continue to develop, the ability to orchestrate multiple specialized AI agents working in concert may become the key differentiator for enterprise AI implementations. In building both the components and the connections between them, Google is betting that the future of AI isn’t just about smarter machines, but about machines that can effectively communicate with each other.