Google Cloud is making an aggressive play to solidify its position in the increasingly competitive artificial intelligence landscape, announcing a sweeping array of new technologies focused on “thinking models,” agent ecosystems, and specialized infrastructure designed specifically for large-scale AI deployments.

At its annual Cloud Next conference in Las Vegas today, Google revealed its seventh-generation Tensor Processing Unit (TPU) called Ironwood, which the company claims delivers more than 42 exaflops of computing power per pod — a staggering 24 times more powerful than the world’s leading supercomputer, El Capitan.

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

The conference comes at a pivotal moment for Google, which has seen considerable momentum in its cloud business. In January, the company reported that its Q4 2024 cloud revenue reached $12 billion, a 30% increase year over year. Google executives say active users in AI Studio and the Gemini API have increased by 80% in just 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” built from the ground up for what it calls “the age of inference” — where the focus shifts from model training to actually using AI systems to solve real-world problems.

The star of Google’s infrastructure announcements is Ironwood, which represents a fundamental shift in chip design philosophy. Unlike previous generations that balanced training and inference, Ironwood was built specifically to run complex AI models after they’ve been trained.

“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 contains more than 9,000 chips and delivers two times better power efficiency than the previous generation. This focus on efficiency addresses one of the most pressing concerns about generative AI: its enormous energy consumption.

In addition to the new chips, Google is opening up its massive global network infrastructure to enterprise customers through Cloud WAN (Wide Area Network). This service makes Google’s 2-million-mile fiber network — the same one that powers consumer services like YouTube and Gmail — available to businesses.

According to Google, Cloud WAN improves network performance by up to 40% while simultaneously reducing total cost of ownership by the same percentage compared to customer-managed networks. This represents an unusual step 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 side, Google is expanding its Gemini model family with Gemini 2.5 Flash, a cost-effective version of its flagship AI system that includes what the company describes as “thinking capabilities.”

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

Google is also significantly expanding its generative media capabilities with updates to Imagen (for image generation), Veo (video), Chirp (audio), and the introduction of Lyria, a text-to-music model. During a demonstration during the press conference, Nenshad Bardoliwalla, Director of Product Management for Vertex AI, showed how these tools could work together to create a promotional video for a concert, complete with custom music and sophisticated editing capabilities 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

Perhaps the most forward-looking announcements focused on creating what Google calls a “multi-agent ecosystem” — an environment where multiple AI systems can work together across different platforms and vendors.

Google is introducing an Agent Development Kit (ADK) that allows developers to build multi-agent systems with less than 100 lines of code. The company is also proposing a new open protocol called Agent2Agent (A2A) that would allow AI agents from different vendors to communicate with each other.

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

More than 50 partners have signed on to support this protocol, including major enterprise software providers like Salesforce, ServiceNow, and SAP, 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 communications to clients — all without writing any code.

From document summaries to drive-thru orders: How Google’s specialized AI agents are affecting industries

Google is also deeply integrating AI across its Workspace productivity suite, with new 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 creates human-like audio versions of documents.

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

In the customer service realm, Google pointed to Wendy’s AI drive-through system, which now handles 60,000 orders daily, and The Home Depot’s “Magic Apron” agent that 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 appears designed to differentiate Google from competitors who may have strengths in specific areas but not 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, the ability to deploy 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 also 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.

For enterprise technical decision makers, these announcements present both opportunities and challenges. 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 talk to each other.