Companies are shifting from gen AI that simply answers questions to autonomous agents that perceive, reason, and act on their behalf. Attempting to scale these agents on legacy stacks exposes structural failures that can lead to fractured governance, a persistent…
Traditional lakehouses were engineered for the era of reporting, not the high-velocity, multimodal demands of AI agents. To bridge this gap, architecture must evolve into an AI-native foundation — one that replaces batch processing with continuous feedback loops and live…
In the agentic era, the role of enterprise data is shifting from a passive resource to a dynamic System of Action. That’s why we announced the Agentic Data Cloud — a unified architecture that integrates models, analytics, and operational databases…
While generative AI sparked a revolution, the true paradigm shift is the rapid evolution from standalone AI models to multi-agent autonomous systems. In this new era, the network transcends basic connectivity to become the critical integration layer for your agentic…
At Google, our TPU design philosophy has always been centered on three pillars: scalability, reliability, and efficiency. As AI models evolve from dense large language models (LLMs) to massive Mixture-of-Experts (MoEs) and reasoning-heavy architectures, the hardware must do more than…
The eighth generation of Google’s TPU includes two specialized chips that will power the future of AI.
Small businesses are the backbone of the global economy. With 400 million SMBs worldwide and 36 million in the U.S. alone, they provide 50% of global employment. Now, with Google Cloud AI, they’re scaling faster, operating more efficiently, and delivering…
Today, we announced the preview of Spanner Omni, a downloadable version of Spanner, that expands its industry-leading distributed database capabilities beyond Google Cloud. This enables enterprises to run Spanner in their own data centers, across clouds, or on a laptop…
The era of agentic AI is accelerating from human- to machine-speed operations, while also creating profound stress on legacy technology infrastructure. This new reality pushes foundational systems to their limits: agents generate thousands of internal messages and complex queries, spawning…
At Google, our TPU design philosophy has always been centered on three pillars: scalability, reliability, and efficiency. As AI models evolve from dense large language models (LLMs) to massive Mixture-of-Experts (MoEs) and reasoning-heavy architectures, the hardware must do more than…
