Google Cloud Next ’25 -system challenge Microsoft and Amazon

Google Cloud has been aggressively trying to consolidate its position in an increasingly competitive artificial intelligence landscape. It has announced an array of new technologies focusing on ” Thinking models“, agent ecosystems and specialized infrastructure specifically designed for large-scale AI implementations.

The annual Google announced its seventh-generation cloud computing system at the Cloud Next conference held in Las Vegas today. Tensor Processing Unit TPU IronwoodThe company claims that it delivers more 42 exaflops per pod —staggeringly 24 times more powerful than world’s leading supercomputer. The Captain.

Amin Vahdat Said, “The opportunity for AI is huge,” during a Google press conference held ahead of the event. Together with our customers, together we’re bringing a new golden era of innovation.

This conference comes at an important time for Google, who has seen significant momentum in its cloud business. In January, Google reported that its Q4 2020 cloud revenue Reached $12 billiona 30% year-over-year increase. Google executives claim that active users in To study & the Gemini API has increased by 80% just in the last month.

How Google’s Ironwood TPUs transform AI computing with power-efficiency

Google positions itself as the only major provider with a “fully AI optimized platform” built from scratch for what it calls the “age of inference,” when the focus shifts away from model training and towards actually using AI systems to resolve real-world issues.

Ironwood is the star of Google’s announcements on infrastructure. It represents a fundamental shift in chip design philosophy. Ironwood, unlike previous generations which balanced training and inferences, was designed to run complex AI after they have been trained. Vahdat said, “It is no longer about what data you put into the model. It’s now about what the model can do after it has been trained.”

Ironwood pods contain more than 9,000 processors and deliver two times the power efficiency of previous generations. This focus on efficiency addresses the biggest concern about generative AI, its enormous energy consumption.

Google is also opening up its global network infrastructure for enterprise customers. Cloud WAN (Wide Area Network). This service allows businesses to access Google’s 2-million mile fiber network, which powers consumer services such as YouTube and Gmail.

Google claims that Cloud WAN can improve network performance by as much as 40%, while reducing total cost of ownership at the same rate. This is a very unusual step for hyperscalers, as they are essentially turning their internal infrastructure into a new product.

Google is expanding the Gemini model family on the software side with the new version 2.5 of Inside Gemini. Gemini 2.5 Flashis a cost-effective, high-performance version of the company’s flagship AI system. It includes “thinking abilities” that break down complex problems using multi-step reasoning, and even self reflection. Gemini 2.5 Pro was launched two weeks ago. It is designed for use cases with high complexity, such as drug discovery and financial modelling. The newly announced Flash version adjusts its reasoning complexity based on the prompt complexity in order to balance performance with cost.

Google also significantly expands its generative media capability with updates to Image (for image generation), I see Chirp (audio) and Lyria – a text to music model – were introduced. Nenshad BARDOLIWALA, Director of Product Management at Vertex AI, demonstrated how these tools can be used together to create a promo video for a concert. The video includes custom music, and advanced editing features like removing unwanted video clips. Bardoliwalla stated that only Vertex AI can bring together all of these models and third-party models on a single platform.

Beyond single AI systems: Google’s multiagent ecosystem aims at enhancing enterprise workflows

One of the most forward-looking announcements was Google’s creation of a ” Multi-agent ecosystem — an environment in which multiple AI systems work together across platforms and vendors.

Google has introduced an “agent ecosystem”. Agent Development Kit (ADK),allows developers to create multi-agent systems in less than 100 lines. The company also proposes a new protocol called Agent2Agent, which allows AI agents from different vendors communicate with each other. Vahdat predicts that 2025 will be the year when generative AI moves from answering simple questions to solving complex issues through agented system.

There are more than 50 partnersincluding major enterprise software companies like Salesforce , ServiceNow SAP has signed on to support the protocolsuggesting a possible industry shift towards interoperable AI systems.

Google is improving its interface for non-technical users. Agent Space is a platform with features such as Agent Gallery provides a single view for all agents available. Agent Designer is a no-code interface to create custom agents. Google demonstrated how a bank account manager could use the tools to analyze client accounts, forecast cash flow problems, and automatically draft communication to clients – all without writing code.

Google’s AI agents can do everything from drive-thru ordering to document summaries.

Google has also integrated AI into its entire business. Workspace is a productivity suite with new features such as ” Help me Analyze (19459119)” in Sheets, a feature that automatically identifies insights in data without formulas or pivot table, and Audio Overviews (Docs), which creates audio versions of documents that sound like they were created by a human.

Google highlighted five categories of specialized agent that are being adopted: customer service work, creative work (data analysis), coding, and security.

Go ogle highlighted Customer Service. Wendy’s AI Drive-Thru System now handles 60,000 daily orders, and The Home Depot ” Magic Apronagent, which provides home improvement advice. Companies like WPPuses Google’s AI for conceptualizing and producing marketing campaigns at a large scale.

Cloud AI competition intensifies. How Google’s comprehensive strategy challenges Microsoft and Amazon.

Google announced its announcements amid increasing competition in the cloud AI area. Microsoft has deeply Amazon has been developing its own technology while OpenAI has been integrated into its Azure platform. Anthropic-powered offering and specialized chips

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

The future of enterprise AI – Why Google’s “thinking models” and interoperability are important for business technology

Google’s AI strategy is comprehensive, covering custom silicon, global networks, model development, agents frameworks, and application integration.

The focus on inference optimization, rather than training capabilities, reflects the maturing AI market. While training ever larger models has dominated headlines in recent years, deploying these models at scale efficiently is the most pressing challenge for enterprise.

Google’s emphasis on interoperability – allowing systems from various vendors to work together – may also signal a move away from the walled gardens that characterized earlier phases in cloud computing. By proposing open protocol like Google’s Agent2Agent is positioning itself to be the connective tissue of a heterogeneous AI eco-system, rather than requiring all-or nothing adoption. These announcements offer both opportunities and challenges to enterprise technical decision-makers. The efficiency gains that are promised by specialized infrastructure such as Ironwood TPUs or Cloud WAN can reduce costs for AI deployments at scale. To navigate the rapidly evolving landscape, careful strategic planning will be required.

As AI systems become more sophisticated, the ability of multiple specialized AI agents to work together may be the key differentiator in enterprise AI implementations. Google believes that the future of AI will not be about smarter machines but machines that can communicate with each other.